The vast availability of free data has been critical to the success of large language models (LLMs). With the widespread use of LLMs, more and more concerns have been raised about the unauthorized use of publicly available data. To protect data from unauthorized use for training models, researchers have proposed adding imperceptible perturbations into image data so that models would be misled by the generated shortcut features and cannot mine information from these images. However, due to the inherent discrete property and semantic complexity of texts, directly applying these methods to text will cause semantic changes, resulting in meaningless shortcut features being constructed. To tackle this problem, in this article, we design a novel Unlearnable text examples generation algorithm via syntax-oriented shortcut (UTE-SS) by incorporating the syntactic structure of texts. Specifically, we propose a syntax template generator (STG) to generate the optimal perturbing syntax for a given category, which will realize imperceptible perturbations. Then, a perturbing text generator (PTG) is designed to perturb the in-class texts with the selected syntax template to stably deviate from the original texts. Along this line, models will be misled to learn the shortcut between the syntax template and the category, so as to keep text examples unlearnable. Extensive experiments over eight advanced Transformer-based pretrained language models (PLMs) on four different natural language processing (NLP) tasks demonstrate the effectiveness and flexibility of our proposed algorithm. Our method is easy to implement, and the code is publicly available at https://github.com/libolb/UTE-SS.

Recommender Systems (RS) have been widely adopted to provide personalized suggestions based on historical user behaviors. However, some users are hesitant to allow RS to learn their preferences at the expense of their privacy information. Therefore, these users prefer to hide their preferences from RS. In this article, we consider this practical yet important question: can privacy-concerned users make RS unavailable to learn their preferences? The challenge lies in achieving this goal while complying with real-world constraints. Normal users still expect accurate recommendations; the scope should target privacy-concerned users. Also, as most companies do not allow users to delete their implicit feedback, the solution cannot rely on data deletion. To this end, we propose a novel ULRec from the perspective of fake interaction generation, a general method for making preferences of privacy-concerned users U n L earnable to personalized Rec ommendation algorithms. First, we formulate the constraints of the data attack based on practical considerations. Then, we define a bi-level optimization process, where the outer loop updates data addition, and the inner loop dynamically updates RS parameters. After that, we propose a loss function that simultaneously considers the requests of both privacy-concerned users and normal users. To ensure the feasible range and model efficiency, we adopt projected gradient descent and automatic differentiation. Finally, extensive experiments on three real-world datasets have demonstrated the effectiveness of our proposed ULRec .

Computerized Adaptive Testing (CAT) is a widely used technology for evaluating learners' proficiency in online education platforms. By leveraging prior estimates of proficiency to select questions and updating the estimates iteratively based on responses, CAT enables personalized learner modeling and has attracted substantial attention. Despite this progress, most existing works focus primarily on improving diagnostic accuracy, while overlooking the selection bias inherent in the adaptive process. Selection Bias arises because the question selection is strongly influenced by the estimated proficiency, such as assigning easier questions to learners with lower proficiency and harder ones to learners with higher proficiency. Since the selection depends on prior estimation, this bias propagates into the diagnosis model, which is further amplified during iterative updates, leading to misalignment and biased predictions. Moreover, the imbalanced nature of learners' historical interactions often exacerbates the bias in diagnosis models. To address this issue, we propose a debiasing framework consisting of two key modules: Cross-Attribute Examinee Retrieval and Selective Mixup-based Regularization. First, we retrieve balanced examinees with relatively even distributions of correct and incorrect responses and use them as neutral references for biased examinees. Then, mixup is applied between each biased examinee and its matched balanced counterpart under label consistency. This augmentation enriches the diversity of bias-conflicting samples and smooths selection boundaries. Finally, extensive experiments on two benchmark datasets with multiple advanced diagnosis models demonstrate that our method substantially improves both the generalization ability and fairness of question selection in CAT.

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion.Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

Abstract Student cognitive modeling is a fundamental task in the intelligence education field. It serves as the basis for various downstream applications, such as student profiling, personalized educational content recommendation, and adaptive testing. Cognitive Diagnosis (CD) and Knowledge Tracing (KT) are two mainstream categories for student cognitive modeling, which measure the cognitive ability from a limited time (e.g., an exam) and the learning ability dynamics over a long period (e.g., learning records from a year), respectively. Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling. However, existing libraries often focus on a particular category and overlook the relationships between them. Additionally, these libraries lack sufficient modularization, which hinders reusability. To address these limitations, we have developed a unified PyTorch-based library EduStudio, which unifies CD and KT for student cognitive modeling. The design philosophy of EduStudio is from two folds. From a horizontal perspective, EduStudio employs the modularization that separates the main step pipeline of each algorithm. From a vertical perspective, we use templates with the inheritance style to implement each module. We also provide eco-services of EduStudio, such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models. Our open-source project is available at the website of edustudio.ai .

Recommender systems face a daunting challenge when entities (users or items) without any historical interactions, known as the ”Completely Cold-Start Problem”. Due to the absence of collaborative signals, Collaborative Filtering (CF) schema fails to deduce user preferences or item characteristics for such cold entities. A common solution is incorporating auxiliary discrete attributes as the bridge to spread collaborative signals to cold entities. Most previous works involve embedding collaborative signals and discrete attributes into different spaces before aligning them for information propagation. Nevertheless, we argue that the separate embedding approach disregards potential high-order similarities between two signals. Furthermore, existing alignment modules typically narrow the geometric-based distance, lacking in-depth exploration of semantic overlap between collaborative signals and cold entities. In this paper, we propose a novel discrete attribute-enhanced completely cold-start recommendation framework, which aims to improve recommendation performance by modeling heterogeneous signals in a unified space. Specifically, we first construct a heterogeneous user-item-attribute graph and capture high-order similarities between heterogeneous signals in a graph-based message-passing manner. To achieve better information alignment, we propose two self-supervised alignment modules from the semantic mutual information and user-item preference perspective. Extensive experiments on six real-world datasets in two types of discrete attribute scenarios consistently verify the effectiveness of our framework.

As one representative framework of self-supervised learning (SSL), contrastive learning (CL) has drawn enormous attention in the representation learning area. By pulling together a “positive” example and an anchor, as well as pushing away many “negative” examples from the anchor, CL is able to generate high-quality representations for the data of different modalities. Therefore, the qualities of selected positive and negative examples are critical for the performance of CL-based models. However, due to the assumption of label unavailability, most existing work follows the paradigm of contrastive instance discrimination, which treats each input instance as an individual category. Therefore, they focused more on positive example generation and designed plenty of data augmentation strategies. For negative examples, they just leverage the in-batch negative sampling strategy. We argue that this negative sampling strategy will easily select false negatives and inhibit the capability of CL, which we also believe is one of the reasons why a large size of negatives is needed in CL. Apart from using annotated labels, we try to tackle this problem in an unsupervised manner. We propose to integrate expectation maximization (EM) into the selection of negative examples and develop a novelEM-enhanced negative sampling strategy(EMCRL) to distinguish false negatives from true ones for CL performance improvement. Specifically,EMCRLemploys EM to estimate the distribution of ground-truth relations between each sample and corresponding in-batch negatives and then optimizes model parameters with the estimations. Considering the sensitivity of EM algorithm to the parameter initialization, we propose to add a random flip into the distribution estimation to enhance the robustness of the learning process. Extensive experiments over several advanced models on sentence representation and image representation tasks demonstrate the effectiveness ofEMCRL. Our method is easy to implement, and the code is publicly available athttps://github.com/zhangkunzk/EMCRL_pytorch.

Programmatic Weak Supervision (PWS) is a recent data labeling paradigm, which employs several Labeling Functions (LFs) to provide weak labels and involves a Label Model (LM) for label aggregation. Despite the significant progress, there still remain some inherent challenges in PWS. From the view of labeling, LFs may wrongly label some data points. From the view of data, some data points themselves may be low-quality (e.g., ambiguous texts or blurred images). These largely stem from the lack of an explicit evaluation mechanism for LFs or data points. To this end, inspired by confident learning focusing on label quality, we propose a Confident PWS (CPWS) approach for high-quality data labeling. Specifically, several LFs are firstly utilized to provide weak labels for unlabeled data. Then, we develop an explicit Dual Evaluation Mechanism (DEM) to evaluate the quality of both LFs and data points, which not only employs data to evaluate trained models but also leverages trained models to evaluate data. Along this line, we further design a Distribution-Guided Pruning Strategy (DPS) to prune low-quality data and aggregate weak labels under the guidance of label class distribution. Extensive experiments on various benchmark datasets demonstrate the effectiveness and generalization ability of our proposed approach.

Cognitive Diagnosis (CD), which leverages students and exercise data to predict students' proficiency levels on different knowledge concepts, is one of fundamental components in Intelligent Education. Due to the scarcity of student-exercise interaction data, most existing methods focus on making the best use of available data, such as exercise content and student information (e.g., educational context). Despite the great progress, the abuse of student sensitive information has not been paid enough attention. Due to the important position of CD in Intelligent Education, employing sensitive information when making diagnosis predictions will cause serious social issues. Moreover, data-driven neural networks are easily misled by the shortcut between input data and output prediction, exacerbating this problem. Therefore, it is crucial to eliminate the negative impact of sensitive information in CD models. In response, we argue that sensitive attributes of students can also provide useful information, and only the shortcuts directly related to the sensitive information should be eliminated from the diagnosis process. Thus, we employ causal reasoning and design a novel Path-Specific Causal Reasoning Framework (PSCRF) to achieve this goal. Specifically, we first leverage an encoder to extract features and generate embeddings for general information and sensitive information of students. Then, we design a novel attribute-oriented predictor to decouple the sensitive attributes, in which fairness-related sensitive features will be eliminated and other useful information will be retained. Finally, we designed a multi-factor constraint to ensure the performance of fairness and diagnosis performance simultaneously. Extensive experiments over real-world datasets (e.g., PISA dataset) demonstrate the effectiveness of our proposed PSCRF.

While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to introduce additional self-supervised signals to address this. However, this approach often unintentionally distances the target user/item from their collaborative neighbors, limiting its efficacy. In response, we propose a solution that treats the collaborative neighbors of the anchor node as positive samples within the final objective loss function. This paper focuses on developing two unique supervised contrastive loss functions that effectively combine supervision signals with contrastive loss. We analyze our proposed loss functions through the gradient lens, demonstrating that different positive samples simultaneously influence updating the anchor node's embeddings. These samples’ impact depends on their similarities to the anchor node and the negative samples. Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model. Our proposedNeighborhood-Enhanced Supervised Contrastive Loss (NESCL)model substitutes the contrastive loss function in SGL with our novel loss function, showing marked performance improvement. On three real-world datasets, Yelp2018, Gowalla, and Amazon-Book, our model surpasses the original SGL by 10.09%, 7.09%, and 35.36% on NDCG@20, respectively.

Social recommendation provides an auxiliary social network structure to enhance recommendation performances. By formulating user-user social network and user-item interaction graph, modern social recommendation architecture is built on learning user and item embeddings into Euclidean space with graph convolution operations. However, the Euclidean space suffers structure distortion when representing the nature power-law distribution of graphs, leading to sub-optimal results for graph based social recommendation. Recently, some studies have explored the alternative of graph embedding learning into hyperbolic space, which can preserve the hierarchy of real-world graphs. However, directly applying current hyperbolic graph embedding models for social recommendation is non-trivial as two challenges: network heterogeneity and social diffusion noise. First, due to the semantic gap existing between social networks and user-item interactions, how to tackle the heterogeneity issue of social recommendation under hyperbolic formulation? Second, explicit modeling of social diffusion easily introduces noise for user preference learning, especially for those active users with amounts of interactions. To tackle the above challenges, in this paper, we propose aHyperbolic Graph Learning based Social Recommendation (HGSR)model. Firstly, we exploit social structure with hyperbolic social embedding pre-training, which could preserve the hierarchical properties of social networks. Secondly, we construct the heterogeneous graph based on user-item interactions and social networks, then treat the pre-trained social embeddings as an additional feature input for user preference learning. Such that, we combine explicit heterogeneous graph learning and implicit feature enhancement for the hyperbolic social recommendation, which can well tackle heterogeneity and social noise issues. We conduct empirical studies on four datasets, and extensive experiments demonstrate the effectiveness of our proposed model compared to state-of-the-art baselines.

Automatic analysis of image sentiment has gained considerable attention with the increasing throughput of user-generated visual contents online. Recently, researchers generally tend to design different Convolutional Neural Networks (CNNs) to extract image content features for sentiment analysis. However, they underestimated the importance of image color, which has been proved very crucial for image sentiment expressing by psychology and art theory. Moreover, we further observe that the coordination of content and color is the main form of image sentiment expressing. Different combinations of content and color could express extremely different sentiments. To that end, in this paper, we propose a Color Enhanced Cross Correlation Net (CECCN), a novel architecture for image sentiment analysis that not only leverages contents and colors simultaneously, but also takes their correlations into consideration. Specifically, we first use a pre-trained CNN to extract content features and color moment to collect color features from multiple color spaces. Then, we propose a novel Cross Correlation (CC) method to model the correlations between content features and color features with attention mechanism and sequence convolution, in which sentiment expressing of content and color can be enhanced by each other. Finally, we integrate these two types of information for better image sentiment analysis. Extensive experiments on two popular and well-studied benchmark datasets demonstrate the superiority and rationality of our proposed CECCN.

Text classification is one of the fundamental tasks in natural language processing, which requires an agent to determine the most appropriate category for input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially pretrained language models (PLMs). Usually, these methods concentrate on input sentences and corresponding semantic embedding generation. However, for another essential component: labels, most existing works either treat them as meaningless one-hot vectors or use vanilla embedding methods to learn label representations along with model training, underestimating the semantic information and guidance that these labels reveal. To alleviate this problem and better exploit label information, in this article, we employ self-supervised learning (SSL) in model learning process and design a novel self-supervised relation of relation ( [Formula: see text]) classification task for label utilization from a one-hot manner perspective. Then, we propose a novel relation of relation learning network( [Formula: see text]-Net) for text classification, in which text classification and [Formula: see text] classification are treated as optimization targets. Meanwhile, triplet loss is employed to enhance the analysis of differences and connections among labels. Moreover, considering that one-hot usage is still short of exploiting label information, we incorporate external knowledge from WordNet to obtain multiaspect descriptions for label semantic learning and extend [Formula: see text]-Net to a novel description-enhanced label embedding network(DELE) from a label embedding perspective. One step further, since these fine-grained descriptions may introduce unexpected noise, we develop a mutual interaction module to select appropriate parts from input sentences and labels simultaneously based on contrastive learning (CL) for noise mitigation. Extensive experiments on different text classification tasks reveal that [Formula: see text]-Net can effectively improve the classification performance and DELE can make better use of label information and further improve the performance. As a byproduct, we have released the codes to facilitate other research.

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub-user group based on users’ sensitive attributes (e.g., gender). Researchers have proposed fairness-aware CF models by decreasing statistical associations between predictions and sensitive attributes. A more natural idea is to achieve model fairness from a causal perspective. The remaining challenge is that we have no access to interventions, i.e., the counterfactual world that produces recommendations when each user has changed the sensitive attribute value. To this end, we first borrow the Rubin-Neyman potential outcome framework to define average causal effects of sensitive attributes. Next, we show that removing causal effects of sensitive attributes is equal to average counterfactual fairness in CF. Then, we use the propensity re-weighting paradigm to estimate the average causal effects of sensitive attributes and formulate the estimated causal effects as an additional regularization term. To the best of our knowledge, we are one of the first few attempts to achieve counterfactual fairness from the causal effect estimation perspective in CF, which frees us from building sophisticated causal graphs. Finally, experiments on three real-world datasets show the superiority of our proposed model.

Recommender systems have been widely used in recent years. By exploiting historical user-item interactions, recommender systems can model personalized potential interests of users and have been widely applied to a wide range of scenarios. Despite their impressive performance, most of them may be subject to unwanted biases related to sensitive attributes (e.g., race and gender), leading to unfairness. An intuitive idea to alleviate this problem is to ensure that there is no mutual information between recommendation results and sensitive attributes. However, keeping independence conditions solely achieves fairness improvement while causing an obvious degradation of recommendation accuracy, which is not a desired result. To this end, in this paper, we re-define recommendation fairness with a novel two-fold mutual information objective. In concerned details, we define fairness as mutual information minimization between embeddings and sensitive information, and mutual information maximization between embeddings and non-sensitive information. Then, a flexible Fair Mutual Information (FairMI) framework is designed to achieve this goal. FairMI first employs a sensitive attribute encoder to capture sensitive information in the data. Then, based on results from the sensitive attribute encoder, an interest encoder is developed to generate sensitive-free embeddings, which are expected to contain rich non-sensitive information of input data. Moreover, we propose novel mutual information (upper/lower) bounds with contrastive information estimation for model optimization. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed FairMI in reducing unfairness and improving recommendation accuracy simultaneously.

Multimedia-based recommendation models learn user and item preference representation by fusing both the user-item collaborative signals and the multimedia content signals. In real scenarios, cold items appear in the test stage without any user interaction record. How to perform cold item recommendation is challenging as the training items and test items have different data distributions. These hybrid preference representations contained auxiliary collaborative signals, so current solutions designed alignment functions to transfer learned hybrid preference representations to cold items. Despite the effectiveness, we argue that they are still limited as these models relied heavily on the manually carefully designed alignment functions, which are easily influenced by the limited item records and noises in the training data.

Cognitive diagnosis is an important task in intelligence education, which aims at measuring students’ proficiency in specific knowledge concepts. Given a fully labeled exercise-concept matrix, most existing models focused on mining students' response records for cognitive diagnosis. Despite their success, due to the huge cost of labeling exercises, a more practical scenario is that limited exercises are labeled with concepts. Performing cognitive diagnosis with limited exercise labels is under-explored and remains pretty much open. In this paper, we propose Disentanglement based Cognitive Diagnosis (DCD) to address the challenges of limited exercise labels. Specifically, we utilize students' response records to model student proficiency, exercise difficulty and exercise label distribution. Then, we introduce two novel modules - group-based disentanglement and limited-labeled alignment modules - to disentangle the factors relevant to concepts and align them with real limited labels. Particularly, we introduce the tree-like structure of concepts with negligible cost for group-based disentangling, as concepts of different levels exhibit different independence relationships. Extensive experiments on widely used benchmarks demonstrate the superiority of our proposed model.

Review information has been demonstrated beneficial for the explainable recommendation. It can be treated as training corpora for generation-based methods or knowledge bases for extraction-based models. However, for generation-based methods, the sparsity of user-generated reviews and the high complexity of generative language models lead to a lack of personalization and adaptability. For extraction-based methods, focusing only on relevant attributes makes them invalid in situations where explicit attribute words are absent, limiting the potential of extraction-based models.

By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering~(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning~(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.

Collaborative filtering based recommendation learns users’ preferences from all users’ historical behavior data, and has been popular to facilitate decision making. Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users’ sensitive attributes (e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. Given each real original user-item interaction record, we propose the following hypotheses for augmenting the training data: each user in one group has a similar item preference (click or non-click) as the item preference of any user in the remaining group. With these hypotheses, we generate “fake" interaction behaviors to complement the original training data. After that, we design a bi-level optimization target, with the inner optimization generates better fake data to augment training data with our hypotheses, and the outer one updates the recommendation model parameters based on the augmented training data. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.

Reading psychology believes text comprehension to involve a complex psychological construction process, with the reader mind being a dynamic associative system that stores an abundance of schemata. For Chinese text, in particular, the unique ideographic writing system allows its lansign to trigger semantic association and schema recalling without the need of phonetics. In contrast to previous research efforts on text classification problems, in this paper we present an interdisciplinary modeling approach that draws inspirations from the cognitive principles of ideography, schema theory and deep learning to study Chinese text classification. Specifically, we first propose a Radical-guided Associative Model (RAM) for preliminary cognitive imitation, which comprises two coupled spaces, namely the Literal Space and Associative Space. Then, taking consideration of the schemata acquired from the mind of a reader which plays a important role in influencing text-dependent information revision, we extend RAM with a systematic Schema-aware Radical-guided Associative Model (SRAM) that embeds label semantics as essential text-independent human knowledge for real-world abstraction. In SRAM, the Schema Space is introduced and a Schema Attention module is proposed with a novel loss paradigm that includes the linkage and interaction between text-dependent prior concepts and text-independent label schemata. Extensive experiments on three real-world datasets demonstrate the effectiveness and rationality of our proposed method.

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as natural language inference (NLI) and paraphrase identification (PI). Much recent progress has been made in this area, especially attention-based methods and pretrained language model-based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of the attention mechanism. In order to overcome this problem and boost the performance of the attention mechanism, we propose a novel dynamic reread (DRr) attention, which can pay close attention to one small region of sentences at each step and reread the important parts for better sentence representations. Based on this attention variation, we develop a novel DRr network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in DRr attention seems insufficient for sentence semantics, and employing pretrained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRr-Net to locally aware dynamic reread attention net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of byte-pair encoding (BPE) in pretrained language models and boost the performance of DRr attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research.

As a vivid and linguistic symbol, Emojis have become a prevailing medium interspersed in text-based communication (e.g., social media and chit-chat) to express emotions, attitudes, and situations. Generally speaking, a social-oriented chatbot that can generate appropriate Emoji-embedded responses would be much more competitive, making communications more fun, engaging, and human-like. However, the current Emoji-related research is still in its infancy, leading to an awkward situation of data deficiency. How to develop an Emoji-embedded dialogue system while addressing the lack of data will be interesting and meaningful for the application of future AI. To bridge this gap, we propose a multi-task learning method for persona-aware Emoji-embedded dialogue generation in this article. Specifically, as the benchmark of model training and evaluation, which includes 1.2 million Emoji-embedded tweets and 1.1 million post-response pairs, we first construct a dataset named EmojiTweet to handle the data deficiency problem. Then, a Seq2Seq-based model with multi-task learning is designed to simultaneously learn response generation and Emoji embedding from the constructed non-Emoji dialogue and Emoji-embedded monologue data. Afterward, we incorporate persona factors into our model by adopting persona fusion and personalized bias methods to deliver personalized dialogues with more accurately selected Emojis. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our model has three key benefits: improved dialogue quality, higher user engagement, and not relying on large-scale Emoji-embedded dialogue data representing specific personas. EmojiTweet will be published publicly via https://mea-lab-421.github.io/EmojiTweet/ .

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing review-based recommendation models enriched user/item embedding learning ability with historical reviews or better modeled user-item interactions with the help of available user-item target reviews. Though significant progress has been made, we argue that current solutions for review-based recommendation suffer from two drawbacks. First, as review-based recommendation can be naturally formed as a user-item bipartite graph with edge features from corresponding user-item reviews, how to better exploit this unique graph structure for recommendation? Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better? To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. Specifically, we first construct a review-aware user-item graph with feature-enhanced edges from reviews, where each edge feature is composed of both the user-item rating and the corresponding review semantics. This graph with feature-enhanced edges can help attentively learn each neighbor node weight for user and item representation learning. After that, we design two additional contrastive learning tasks (i.e., Node Discrimination and Edge Discrimination) to provide self-supervised signals for the two components in recommendation process. Finally, extensive experiments over five benchmark datasets demonstrate the superiority of our proposed RGCL compared to the state-of-the-art baselines.

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models.

Emotion Cause Extraction (ECE) aims to reveal the cause clauses behind a given emotion expressed in a text, which has become an emerging topic in broad research communities, such as affective computing and natural language processing. Despite the fact that current methods about the ECE task have made great progress in text semantic understanding from lexicon- and sentence-level, they always ignore the certain causal narratives of emotion text. Significantly, these causal narratives are presented in the form of semantic structure and highly helpful for structure-level emotion cause understanding. Nevertheless, causal narrative is just an abstract narratological concept and its involving semantics is quite different from the common sequential information. Thus, how to properly model and utilize such particular narrative information to boost the ECE performance still remains an unresolved challenge. To this end, in this paper, we propose a novel Causal Narrative Comprehension Model (CNCM) for emotion cause extraction, which learns and leverages causal narrative information smartly to address the above problem. Specifically, we develop a Narrative-aware Causal Association (NCA) unit, which mines the narrative cue about emotional results and uses the semantic correlation between causes and results to model causal narratives of documents. Besides, we design a Result-aware Emotion Attention (REA) unit to make full use of the known result of causal narrative for multiple understanding about emotional causal associations. Through the ingenious combination and collaborative utilization of these two units, we could better identify the emotion cause in the text with causal narrative comprehension. Extensive experiments on the public English and Chinese benchmark datasets of ECE task have validated the effectiveness of CNCM with significant margin by comparing with the state-of-the-art baselines, which demonstrates the potential of narrative information in long text understanding.

Recent years have witnessed a successful rise of the time synchronizedgossiping comment, or so-called danmu combined with online videos. This new business mode has enriched communication among users by sending users’ feelings through danmus and sharing these danmus on time synchronized videos. Can danmu communication be helpful for better user behavior modeling or video analyzing? To this question, in this article, preliminary attempts are made on analysis of users and videos by introducing aDanmudataset which is collected from a real-world danmu-enabled video sharing platform. The dataset contains 1.7 TB of videos and danmus in total across eight video categories. With a focus on the 7.9 million danmus records and 4.8 million video frames, we first perform the basic statistic analysis and high-level semantic analysis. After that, we show some of the previous work on this area, including user behavior modeling, fine-grained video understanding and labeling, video plot generation and image-enhanced semantic understanding. For each application, we also propose its possible future directions. We hope this new dataset will inspire new ideas in areas among language, multimedia, and user understanding.

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation text generation. As most of the reviews are general text without detailed evaluation, some researchers leveraged auxiliary information of users or items to enrich the generated explanation text. Nevertheless, the auxiliary data is not available in most scenarios and may suffer from data privacy problems. In this article, we argue that the reviews contain abundant semantic information to express the users’ feelings for various aspects of items, while these information are not fully explored in current explanation text generation task. To this end, we study how to generate more fine-grained explanation text in review based recommendation without any auxiliary data. Though the idea is simple, it is non-trivial since the aspect is hidden and unlabeled. Besides, it is also very challenging to inject aspect information for generating explanation text with noisy review input. To solve these challenges, we first leverage an advanced unsupervised neural aspect extraction model to learn the aspect-aware representation of each review sentence. Thus, users and items can be represented in the aspect space based on their historical associated reviews. After that, we detail how to better predict ratings and generate explanation text with the user and item representations in the aspect space. We further dynamically assign review sentences which contain larger proportion of aspect words with larger weights to control the text generation process, and jointly optimize rating prediction accuracy and explanation text generation quality with a multi-task learning framework. Finally, extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model for both recommendation accuracy and explainability.

Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact, subconsciously using the associative information guided by radicals is a key for readers to ensure the robustness of semantic understanding. Fortunately, many basic and extended concepts related to radicals are systematically included in Chinese language dictionaries, which leaves a handy but unexplored way for improving Chinese text representation and classification. To this end, we draw inspirations from cognitive principles between ideography and human associative behavior to propose a novel Radical-guided Associative Model (RAM) for Chinese text classification. RAM comprises two coupled spaces, namely Literal Space and Associative Space, which imitates the real process in people's mind when understanding a Chinese text. To be specific, we first devise a serialized modeling structure in Literal Space to thoroughly capture the sequential information of Chinese text. Then, based on the authoritative information provided by Chinese language dictionaries, we design an association module and put forward a strategy called Radical-Word Association to use ideographic radicals as the medium to associate prior concept words in Associative Space. Afterwards, we design an attention module to imitate people's matching and decision between Literal Space and Associative Space, which can balance the importance of each associative words under specific contexts. Finally, extensive experiments on two real-world datasets prove the effectiveness and rationality of RAM, with good cognitive insights for future language modeling.

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite the effectiveness of these models, most of them treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about labels. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches.

Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually generate an initial image with sentence embedding and then refine it with fine-grained word embedding. Despite the significant progress, the ‘aspect’ information (e.g., red eyes) contained in the text, referring to several words rather than a word that depicts ‘a particular part or feature of something’, is often ignored, which is highly helpful for synthesizing image details. How to make better utilization of aspect information in text-to-image synthesis still remains an unresolved challenge. To address this problem, in this paper, we propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Moreover, inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed. AGR utilizes word-level embedding to globally enhance the previously generated image, while ALR dynamically employs aspect-level embedding to refine image details from a local perspective. Finally, a corresponding matching loss function is designed to ensure the text-image semantic consistency at different levels. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and COCO) demonstrate the superiority and rationality of our method.

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard'' unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively.

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors are treated as unlinked edges in the user-item bipartite graph. As users' unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference links. In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other in CF, as updated node embeddings are learned from previous graph structure, and vice versa ~(i.e., newly updated graph structure are optimized based on current node embedding results). Some previous works provided approaches to refine the graph structure. However, most of these graph learning models relied on node features for modeling, which are not available in CF. Besides, nearly all optimization goals tried to compare the learned adaptive graph and the original graph from a local reconstruction perspective, whether the global properties of the adaptive graph structure are modeled in the learning process is still unknown. To this end, in this paper, we propose an enhanced graph learning network EGLN approach for CF via mutual information maximization. The key idea of EGLN is two folds: First, we let the enhanced graph learning module and the node embedding module iteratively learn from each other without any feature input. Second, we design a local-global consistency optimization function to capture the global properties in the enhanced graph learning process. Finally, extensive experimental results on three real-world datasets clearly show the effectiveness of our proposed model.

Natural language inference (NLI) task requires an agent to determine the semantic relation between a premise sentence ( p) and a hypothesis sentence ( h), which demands sufficient understanding about sentences semantic. Due to the issues, such as polysemy, ambiguity, as well as fuzziness of sentences, intense sentence understanding is very challenging. To this end, in this article, we introduce the corresponding image of sentences as reference information for enhancing sentence semantic understanding and representing. Specifically, we first propose an image-enhanced multilevel sentence representation net (IEMLRN), that utilizes the image features from pretrained models for enhancing the sentence semantic understanding at different scales, i.e., lexical-level, phrase-level, and sentence-level. The proposed model advances the performance on NLI tasks by leveraging the pretrained global features of images. However, as these pretrained image features are optimized on specific image classification datasets, they may not show the best performance on NLI tasks. Therefore, we further propose to design an adaptive image feature generator that extracts fine-grained image labels from the corresponding sentences. After that, we extend the IEMLRN to multilevel image-enhanced sentence representation net (MIESR) by utilizing not only the coarse-grained pretrained features of images, but also the fine-grained adaptive features of images. Therefore, sentence semantic can be evaluated and enhanced more comprehensively and precisely. Extensive experiments on two benchmark datasets (SNLI, SICK) clearly show our proposed IEMLRN significantly outperform the state-of-the-art baselines, and our proposed MIESR model achieves the best performance by considering not only the text but also images in an adaptive multigranularities way.

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://github.com/newlei/LR-GCCF.

Visual Emotion Recognition has attracted more and more research attention in recent years. Existing approaches mainly depend on facial expression or analyze the whole image between positive and negative. Actually, people can recognize multiple emotions from one image based on global and 10-cal information. In this paper, we propose a Context-Aware Generation-Based Net (CAGBN), a novel architecture that makes full use of global and local information of the image by considering both the whole image and details of the target person. Inspired by psychological studies that when viewing a person in his situation, we tend to give judgments gradually rather than assign all labels at the same time, CAGBN transforms the multi-label classification problem into a sequence generation task for better recognition. Extensive experimental results on the emotion recognition dataset demonstrate the superiority and rationality of CAGBN.

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user(item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user~(item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks.

In many recommender systems, users express item opinions through two kinds of behaviors: giving preferences and writing detailed reviews. As both kinds of behaviors reflect users’ assessment of items, review enhanced recommender systems leverage these two kinds of user behaviors to boost recommendation performance. On the one hand, researchers proposed to better model the user and item embeddings with additional review information for enhancing preference prediction accuracy. On the other hand, some recent works focused on automatically generating item reviews for recommendation explanations with related user and item embeddings. We argue that, while the task of preference prediction with the accuracy goal is well recognized in the community, the task of generating reviews for explainable recommendation is also important to gain user trust and increase conversion rate. Some preliminary attempts have considered jointly modeling these two tasks, with the user and item embeddings are shared. These studies empirically showed that these two tasks are correlated, and jointly modeling them would benefit the performance of both tasks.

Social networking services provide platforms for users to perform two kinds of behaviors: consumption behavior (e.g., recommending items of interest) and social link behavior (e.g., recommending potential social links). Accurately modeling and predicting users’ two kinds of behaviors are two core tasks in these platforms with various applications. Recently, with the advance of neural networks, many neural-based models have been designed to predict a single users’ behavior, i.e., social link behavior or consumption behavior. Compared to the classical shallow models, these neural-based models show better performance to drive a user’s behavior by modeling the complex patterns. However, there are few works exploiting whether it is possible to design a neural-based model to jointly predict users’ two kinds of behaviors to further enhance the prediction performance. In fact, social scientists have already shown that users’ two kinds of behaviors are not isolated; people trend to the consumption recommendation of friends on social platforms and would like to make new friends with like-minded users. While some previous works jointly model users’ two kinds of behaviors with shallow models, we argue that the correlation between users’ two kinds of behaviors are complex, which could not be well-designed with shallow linear models. To this end, in this article, we propose a neural joint behavior prediction model named Neural Joint Behavior Prediction Model (NJBP) to mutually enhance the prediction performance of these two tasks on social networking platforms. Specifically, there are two key characteristics of our proposed model: First, to model the correlation of users’ two kinds of behaviors, we design a fusion layer in the neural network to model the positive correlation of users’ two kinds of behaviors. Second, as the observed links in the social network are often very sparse, we design a new link-based loss function that could preserve the social network topology. After that, we design a joint optimization function to allow the two behaviors modeling tasks to be trained to mutually enhance each other. Finally, extensive experimental results on two real-world datasets show that our proposed method is on average 7.14% better than the best baseline on social link behavior while 6.21% on consumption behavior prediction. Compared with the pair-wise loss function on two datasets, our proposed link-based loss function improves at least 4.69% on the social link behavior prediction and 4.72% on the consumption behavior prediction.

Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and arrangement, most of these methods have limitations when applying to multi-track music generation. Some critical factors related to the quality of music are not well addressed, such as chord progression, rhythm pattern, and musical style. In order to tackle the problems and ensure the harmony of multi-track music, in this article, we propose an end-to-end melody and arrangement generation framework to generate a melody track with several accompany tracks played by some different instruments. To be specific, we first develop a novel Chord based Rhythm and Melody Cross-Generation Model to generate melody with a chord progression. Then, we propose a Multi-Instrument Co-Arrangement Model based on multi-task learning for multi-track music arrangement. Furthermore, to control the musical style of arrangement, we design a Multi-Style Multi-Instrument Co-Arrangement Model to learn the musical style with adversarial training. Therefore, we can not only maintain the harmony of the generated music but also control the musical style for better utilization. Extensive experiments on a real-world dataset demonstrate the superiority and effectiveness of our proposed models.

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.

Personalized news recommendation is important to help users find their interested news and improve reading experience.A key problem in news recommendation is learning accurate user representations to capture their interests.Users usually have both long-term preferences and short-term interests.However, existing news recommendation methods usually learn single representations of users, which may be insufficient.In this paper, we propose a neural news recommendation approach which can learn both long-and short-term user representations.The core of our approach is a news encoder and a user encoder.In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words.In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network.Besides, we propose two methods to combine long-term and short-term user representations.The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation.The second one is concatenating both long-and short-term user representations as a unified user vector.Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

Natural Language Inference (NLI) task requires an agent to determine the semantic relation between a premise sentence (p) and a hypothesis sentence (h), which demands sufficient understanding about sentences from lexical knowledge to global semantic. Due to the issues such as polysemy, ambiguity, as well as fuzziness of sentences, fully understanding sentences is still challenging. To this end, we propose an Image-Enhanced Multi-Level Sentence Representation Net (IEMLRN), a novel architecture that is able to utilize the image to enhance the sentence semantic understanding at different scales. To be specific, we introduce the corresponding image of sentences as reference information, which can be helpful for sentence semantic understanding and inference relation evaluation. Since image information might be related to the sentence semantics at different scales, we design a multi-level architecture to understand sentences from different granularity and generate the sentence representation more precisely. Experimental results on the large-scale NLI corpus and real-world NLI alike corpus demonstrate that IEMLRN can simultaneously improve the performance. It is noteworthy that IEMLRN significantly outperforms the state-of-the-art sentence-encoding based models on the challenging hard subset and challenging lexical subset of SNLI corpus.

With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.

Recognizing lexical entailment (RLE) always plays an important role in inference of natural language, i.e., identifying whether one word entails another, for example, fox entails animal. In the literature, automatically recognizing lexical entailment for word pairs deeply relies on words' contextual representations. However, as a "prototype" vector, a single representation cannot reveal multifaceted aspects of the words due to their homonymy and polysemy. In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment. To be specific, we first utilize multiple embedding vectors from different contexts to represent the input word pairs. Then, through different combination methods and attention mechanism, we integrate different embedding vectors and optimize their weights to predict whether there are entailment relations in word pairs. Moreover, our proposed framework is flexible and open to handle different word contexts and entailment perspectives in the text corpus. Extensive experiments on five datasets show that our approach significantly improves the performance of automatic RLE in comparison with several state-of-the-art methods.