Bundle recommender system is divided into two main stages: bundle editing and bundle recommendation. While substantial research progress has been made in each stage, in practical application scenarios, bundle compositions and the final recommended bundles mutually influence each other: the continuously editing bundle compositions affect the recommendation results, while user feedback on recommended bundles in turn guides the refinement of bundle compositions. This paper presents the first comprehensive reproducibility study of the complete bundle recommendation pipeline. We implement eight bundle-level editing methods, nine item-level editing methods, and seven state-of-the-art bundle recommendation models, and evaluate their performance across six real-world datasets. Our empirical analysis reveals several key findings. First, bundle-level editing faces the challenge of generating high-quality bundles. Second, in the item-level editing, the replacement operation emerges as a universal bottleneck across all methods. Third, in the recommendation stage, recommendation models exhibit varying performance across different interaction density scenarios (e.g., cold-start). Finally, bundle recommendation suffers degraded performance when integrating item-level editing and bundle recommendation within a unified pipeline. Overall, there is the systemic limitation of bundle recommendation: prior work has focused on optimizing individual stages independently, disregarding the interdependencies throughout the entire recommendation system. These findings highlight the urgent need to develop end-to-end solutions that can holistically address the bundle editing and recommendation workflow. Our repository is now available for public access via https://github.com/anyr123/Bundle_Edit_Rec_SIGIR26.

Energy efficiency has become a critical requirement, driving recommendation systems for resource-constrained environments such as edge devices. Model quantization offers an effective way to build low-bitwidth models while preserving accuracy. However, user–item interaction graphs contain numerous nodes and complex topological structures, leading nodes to exhibit unique similarities and differences. Existing quantization methods uniformly process parameters in high-dimensional DNN layers (e.g., linear, convolutional, or attention layers), while inadequately capturing such similarities among node embeddings. This paper proposes GraphQ, a chunk-wise quantization framework for graph collaborative filtering that supports both the training and post-training phases in a unified perspective. Our core idea is to adaptively partition node embeddings into multiple chunks based on the distribution of embedding values, and then apply chunk-wise quantization. Specifically, for quantization-aware training (QAT), we introduce learnable low-precision quantization factors that partition node embeddings into multiple chunks and are dynamically updated following message passing. For post-training quantization (PTQ), we first cluster nodes and then partition their dimensions into chunks for weight clipping. Extensive experiments on four real-world datasets show that GraphQ outperforms state-of-the-art QAT methods by an average of 27.49% in Recall@10 under the 256-dimensional embedding and 2-bit settings, and surpasses PTQ methods by 78.64% on average under 4-bit settings.

Sequential recommendation models, particularly those based on attention, achieve strong accuracy but incur quadratic complexity, making long user histories prohibitively expensive. Sub-quadratic operators such as Hyena provide efficient alternatives in language modeling, but their potential in recommendation remains underexplored. We argue that Hyena faces challenges in recommendation due to limited representation capacity on sparse, long user sequences. To address these challenges, we propose HyenaRec, a novel sequential recommender that integrates polynomial-based kernel parameterization with gated convolutions. Specifically, we design convolutional kernels using Legendre orthogonal polynomials, which provides a smooth and compact basis for modeling long-term temporal dependencies. A complementary gating mechanism captures fine-grained short-term behavioral bursts, yielding a hybrid architecture that balances global temporal evolution with localized user interests under sparse feedback. This construction enhances expressiveness while scaling linearly with sequence length. Extensive experiments on multiple real-world datasets demonstrate that HyenaRec consistently outperforms Attention-, Recurrent-, and other baselines in ranking accuracy. Moreover, it trains significantly faster (up to 6× speedup), with particularly pronounced advantages on long-sequence scenarios where efficiency is maintained without sacrificing accuracy. These results highlight polynomial-based kernel parameterization as a principled and scalable alternative to attention for sequential recommendation.

Cold-start bundle recommendation focuses on modeling new bundles with insufficient information to provide recommendations. Advanced bundle recommendation models usually learn bundle representations from multiple views (e.g., user-bundle interaction views) at both bundle and item levels. Consequently, the cold-start problem for bundles is more challenging than that for traditional items due to the dual-level multi-view complexity. For cold-start bundle recommendation, we propose a novel Mixture of Diffusion Experts (MoDiffE) framework, which employs a divide-and-conquer strategy and consists of three parts: (1) Division : The bundle cold-start problem is divided into view-specific but unified sub-problems: the poor representation of feature-missing bundles in prior-embedding models. (2) Conquest : Diffusion models uniformly solve all sub-problems by directly generating diffusion representations without depending on specific features. (3) Combination : A cold-aware hierarchical Mixture of Experts (MoE) is employed to adaptively combine results of the sub-problems into final recommendations. Additionally, MoDiffE proposes a cold-start gating augmentation method to enable gating for cold bundles. In experiments on three real-world datasets, MoDiffE significantly outperforms existing solutions in cold-start bundle recommendation. It achieves up to a 0.1027 Recall@20 improvement in cold-start scenarios and up to a 47.43% relative improvement in all-bundle scenarios.

In the realm of collaborative filtering recommendation systems, Graph Neural Networks (GNNs) have demonstrated remarkable performance but face significant challenges in deployment on resource-constrained edge devices due to their high embedding parameter requirements and computational costs. Using common quantization method directly on node embeddings may overlooks their graph based structure, causing error accumulation during message passing and degrading the quality of quantized embeddings.To address this, we propose Graph based Node-Aware Dynamic Quantization training for collaborative filtering (GNAQ), a novel quantization approach that leverages graph structural information to enhance the balance between efficiency and accuracy of GNNs for Top-K recommendation. GNAQ introduces a node-aware dynamic quantization strategy that adapts quantization scales to individual node embeddings by incorporating graph interaction relationships. Specifically, it initializes quantization intervals based on node-wise feature distributions and dynamically refines them through message passing in GNN layers. This approach mitigates information loss caused by fixed quantization scales and captures hierarchical semantic features in user-item interaction graphs. Additionally, GNAQ employs graph relation-aware gradient estimation to replace traditional straight-through estimators, ensuring more accurate gradient propagation during training. Extensive experiments on four real-world datasets demonstrate that GNAQ outperforms state-of-the-art quantization methods, including BiGeaR and N2UQ, by achieving average improvement in 27.8% Recall@10 and 17.6% NDCG@10 under 2-bit quantization. In particular, GNAQ is capable of maintaining the performance of full-precision models while reducing their model sizes by 8 to 12 times; in addition, the training time is twice as fast compared to quantization baseline methods.

With the rise of generative models, video-language cross-modal applications have seen significant growth. Generating procedural text from instructional videos has become a crucial task, playing a key role in both understanding visual scenes and supporting practical applications. The sequential nature of video clips is particularly important, as entities may appear across multiple clips, reflecting fine-grained intra-modal self-similarity. However, most existing training methods treat other clips in a sequence as negative samples when a target is specified, neglecting their step-wise correlations. To address this limitation, we introduce Step-wise Soft Alignment via OpTimal TrAnsport (SATA), which constructs soft positive pairs to mitigate the issue. SATA first generates a step-wise similarity matrix by leveraging visual representations and generated procedural text. It then aligns the step-wise distributions between procedural text and video clips using optimal transport. The resulting transport distance serves as a weight, treating these pairs as soft positives for contrastive learning, ultimately improving the accuracy of procedural text generation. Our experiments on the publicly available YouCookII and ActivityNet Captions datasets demonstrate the effectiveness of SATA, achieving absolute improvements of 0.5% to 1.7% and 0.6% to 14.9% in paragraph-level evaluation, respectively.

Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge across domains.Disentangled representation learning provides an effective solution to model complex user preferences by separating intra-domain features (domainshared and domain-specific features), thereby enhancing robustness and interpretability.However, disentanglement-based CDR methods employing generative modeling or GNNs with contrastive objectives face two key challenges: (i) pre-separation strategies decouple features before extracting collaborative signals, disrupting intra-domain interactions and introducing noise; (ii) unsupervised disentanglement objectives lack explicit task-specific guidance, resulting in limited consistency and suboptimal alignment.To address these challenges, we propose DGCDR, a GNN-enhanced encoder-decoder framework.To handle challenge (i), DGCDR first applies GNN to extract high-order collaborative signals, providing enriched representations as a robust foundation for disentanglement.The encoder then dynamically disentangles features into domain-shared and -specific spaces, preserving collaborative information during the separation process.To handle challenge (ii), the

This is an erratum for the article "A Dual Perspective Framework of Knowledge-correlation for Cross-domain Recommendation" published in ACM Trans. Knowl. Discov. Data 18(6): 152:1-152:28 (2024).

Meal recommendation, as a typical health-related recommendation task, contains complex relationships between users, courses, and meals. Among them, meal-course affiliation associates user-meal and user-course interactions. However, an extensive literature review demonstrates that there is a lack of publicly available meal recommendation datasets including meal-course affiliation. Meal recommendation research has been constrained in exploring the impact of cooperation between two levels of interaction on personalization and healthiness. To pave the way for meal recommendation research, we introduce a new benchmark dataset called MealRec^+. Due to constraints related to user health privacy and meal scenario characteristics, the collection of data that includes both meal-course affiliation and two levels of interactions is impeded. Therefore, a simulation method is adopted to derive meal-course affiliation and user-meal interaction from the user's dining sessions simulated based on user-course interaction data. Then, two well-known nutritional standards are used to calculate the healthiness scores of meals. Moreover, we experiment with several baseline models, including separate and cooperative interaction learning methods. Our experiment demonstrates that cooperating the two levels of interaction in appropriate ways is beneficial for meal recommendations. The dataset is available on GitHub (https://github.com/WUT-IDEA/MealRecPlus).

Cross-domain recommendation (CDR) aims to utilize the information from relevant domains to guide the recommendation task in the target domain, and shows great potential in alleviating the data sparsity and cold-start problems of recommender systems. Most existing methods utilize the interaction information (e.g., ratings and clicks) or consider auxiliary information (e.g., tags and comments) to analyze the users' cross-domain preferences, but such kinds of information ignore the intrinsic semantic relationship of different domains. In order to effectively explore the inter-domain correlations, encyclopedic knowledge graphs (KG) involving different domains are highly desired in cross-domain recommendation tasks because they contain general information covering various domains with structured data format. However, there are few datasets containing KG information for CDR tasks, so in order to enrich the available data resource, we build a KG-enhanced cross-domain recommendation dataset, named Amazon-KG, based on the widely used Amazon dataset for CDR and the well-known KG DBpedia. In this work, we analyze the potential of KG applying in cross-domain recommendations, and describe the construction process of our dataset in detail. Finally, we perform quantitative statistical analysis on the dataset. We believe that datasets like Amazon-KG contribute to the development of knowledge-aware cross-domain recommender systems. Our dataset has been released at https://github.com/WangYuhan-0520/Amazon-KG-v2.0-dataset.

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN ( RepDRL-DDQN ) and Dueling DDQN ( RepDRL-DDDQN ) versions of the DRL model surpass the baseline performance, with the proposed model’s learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model’s performance, ultimately providing an optimal treatment policy focused on rapid patient recovery.

Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user’s preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user’s long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be evaluated? Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be guided in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy.

Recommender System provides users with online services in a personalized way. The performance of traditional recommender systems may deteriorate because of problems such as cold-start and data sparsity. Cross-domain Recommendation System utilizes the richer information from auxiliary domains to guide the task in the target domain. However, direct knowledge transfer may lead to a negative impact due to data heterogeneity and feature mismatch between domains. In this article, we innovatively explore the cross-domain correlation from the perspectives of content semanticity and structural connectivity to fully exploit the information of Knowledge Graph. First, we adopt domain adaptation that automatically extracts transferable features to capture cross-domain semantic relations. Second, we devise a knowledge-aware graph neural network to explicitly model the high-order connectivity across domains. Third, we develop feature fusion strategies to combine the advantages of semantic and structural information. By simulating the cold-start scenario on two real-world datasets, the experimental results show that our proposed method has superior performance in accuracy and diversity compared with the SOTA methods. It demonstrates that our method can accurately predict users’ expressed preferences while exploring their potential diverse interests.

Machine Unlearning, a pivotal field addressing data privacy in machine learning, necessitates efficient methods for the removal of private or irrelevant data. In this context, significant challenges arise, particularly in maintaining privacy and ensuring model efficiency when managing outdated, private, and irrelevant data. Such data not only compromises model accuracy but also burdens computational efficiency in both learning and unlearning processes. To mitigate these challenges, we introduce a novel framework: Attention-based Machine Unlearning using Federated Reinforcement Learning (FRAMU). This framework incorporates adaptive learning mechanisms, privacy preservation techniques, and optimization strategies, making it a well-rounded solution for handling various data sources, either single-modality or multi-modality, while maintaining accuracy and privacy. FRAMU's strengths include its adaptability in fluctuating data landscapes, its ability to unlearn outdated, private, or irrelevant data, and its support for continual model evolution without compromising privacy. Our experiments, conducted on both single-modality and multi-modality datasets, revealed that FRAMU significantly outperformed baseline models. Additional assessments of convergence behavior and optimization strategies further validate the framework's utility in federated learning applications. Overall, FRAMU advances Machine Unlearning by offering a robust, privacy-preserving solution that optimizes model performance while also addressing key challenges in dynamic data environments.

Sequential prediction has great value for resource allocation due to its capability in analyzing intents for next prediction. A fundamental challenge arises from real-world interaction dynamics where similar sequences involving multiple intents may exhibit different next items. More importantly, the character of volume candidate items in sequential prediction may amplify such dynamics, making deep networks hard to capture comprehensive intents. This article presents a sequential prediction framework with Decoupled Progressive Distillation (DePoD), drawing on the progressive nature of human cognition. We redefine target and non-target item distillation according to their different effects in the decoupled formulation. This can be achieved through two aspects: (1) Regarding how to learn, our target item distillation with progressive difficulty increases the contribution of low-confidence samples in the later training phase while keeping high-confidence samples in the earlier phase. And, the non-target item distillation starts from a small subset of non-target items from which size increases according to the item frequency. (2) Regarding whom to learn from, a difference evaluator is utilized to progressively select an expert that provides informative knowledge among items from the cohort of peers. Extensive experiments on four public datasets show DePoD outperforms state-of-the-art methods in terms of accuracy-based metrics.

Visual information shows to empower accurately named entity recognition in short texts, such as posts from social media. Previous work on multimodal named entity recognition (MNER) often regards an image as a set of visual objects, trying to explicitly align visual objects and entities. However, these methods may suffer the bias introduced by visual objects when they are not identical to entities in quantity and entity type. Different from this kind of explicit alignment, we argue that implicit alignment is effective in optimizing the shared semantic space learning between text and image for improving MNER. To this end, we propose a de-bias contrastive learning based approach for MNER, which studies modality alignment enhanced by cross-modal contrastive learning. Specifically, our contrastive learning adopts a hard sample mining strategy and a debiased contrastive loss to alleviate the bias of quantity and entity type, respectively, which globally learns to align the feature spaces from text and image. Finally, the learned semantic space works with a NER decoder to recognize entities in text. Conducted on two benchmark datasets, experimental results show that our approach outperforms the current state-of-the-art methods.

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information from knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although some works boost the performance and explainability of recommendations through modeling the user's temporal sequential behavior, most of them either only focus on modeling the user's sequential interactions within a path or independently and separately of the recommendation mechanism. Moreover, some path-based explainable recommendations use random selection or traditional machine learning methods to decrease the volume of explainable paths, which cannot guarantee high quality of the explainable paths for the recommendation. To deal with the problem, recent path exploration use reinforcement learning to improve diversity and quality. However, unsupervised training leads to low-efficiency path exploration. Therefore, we propose a novelTemporalMeta-path GuidedExplainableRecommendation leveragingReinforcementLearning (TMER-RL), which utilizes supervised reinforcement learning to explore item-item paths between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on a dynamic knowledge graph for the explainable recommendation. Extensive evaluations of TMER-RL on two real-world datasets show state-of-the-art performance compared to recent strong baselines.

Sponsored online advertising delivers many billions of revenues for online ads publishers. The ads systems take userinput query keywords and display ads that are relevant to the query. the task of click-through rate (CTR) prediction aims to estimate the likelihood of a user clicking on the ads, which has become one of the core goals in the ads system. In order to further improve the CTR, user portraits are also considered as an input to make personalized ads display and recommendations, in the current deep learning CTR training platform. The naive combination of user space (~ 109) and feature space (~ 10]12however, would yield a 1021dimensional space. It is not only infeasible to feed the 1021parameters into the embedding layer with any off-the-shelf storage, but also impractical to train the network in such massive-scale dimensional space. In this paper, we design a novel CTR prediction framework for ads systems to tackle the massive-scale user-feature combination challenge. Specifically, we introduce a feature fusion network to explicitly learn user-feature cross embedding in an end-to-end manner. To improve the efficiency, we prune the feature fusion networks to a practical number through a network importance ranking scheme. Extensive empirical experiments on Baidu’s ads data validate the effectiveness of the proposed feature fusion networks.

Adaptive traffic signal control is an important and challenging real-world problem that fits well with the task framework of deep reinforcement learning. As one of the critical design elements, the environmental state plays a crucial role in traffic signal control decisions. The state definitions of most existing works mostly contain lane-level queue length, intersection phase, and other features. However, these works are heuristically designed in representing states. This results in highly sensitive and unstable performances of next actions. The paper proposes a Cooperative Max-Pressure enhanced State Learning for the traffic signal control (CMP-SL), which is inspired by the advanced pressure definition for an intersection in the transportation field to cope with this problem. First, our CMP-SL explicitly extends the cooperative max-pressure to the state definition of a target intersection, aiming to obtain accurate environment information by including the traffic pressures of surrounding intersections. From then on, a graph attention mechanism (GAT) is used to learn the state representation of the target intersection in our spatial-temporal state module. Second, since the state is coupled with the reward in reinforcement learning, our method takes the cooperative max-pressure of the target intersection into the reward definition. Furthermore, a temporal convolutional network (TCN) based sequence model is used to capture the historical state of traffic flow. And the historical spatial-temporal and the current spatial state features are concatenated into a DQN network to predict the Q value and generate each phase action. Finally, experiments with two real-world traffic datasets demonstrate that our method achieves shorter vehicle average times and higher network throughput than the state-of-the-art models.

In social media analysis, gender prediction is one of the most important tasks of user profiling. Web users often post messages in a timeline manner to record their living moments. These messages containing texts and images, constitute long multi-modal data that potentially represents the living style, preference, or opinion regarding users. Therefore, it is feasible to predict the gender of a user by utilizing such living moments. However, the rich modalities (time, length, text, and image) of living moments with difficult challenges have not been fully exploited by the research communities for practical applications. To this end, we propose a novel gender prediction framework based on user-posted living Moments MomNet). The MomNet mainly consists of a moment memory module and a central executive module inspired by the two characteristics of working memory theory. One is that humans can associate related information to facilitate memory. Our moment memory module aggregates similar uni-modal moments of a user to form different chunks and encode the chunks into moment memory representations. The other is that humans coordinate information from different modalities to make judgments. Our central executive module is designed to coordinate comprehensive attentions of moment memory representations from texts, images, and their combinations. Finally, a softmax classifier is used to predict gender. Extensive experiments conducted on a real-world public dataset show that our framework achieves 86.63% accuracy and outperforms all state-of-the-art methods in terms of accuracy.

This paper presents a novel approach to the problem of event-dense text and image cross-modal retrieval where the text contains the descriptions of numerous events. It is known that modality alignment is crucial for retrieval performance. However, due to the lack of event sequence information in the image, it is challenging to perform the fine-grain alignment of the event-dense text with the image. Our proposed approach incorporates the event-oriented features to enhance the cross-modal alignment, and applies the event-dense text-image retrieval to the food domain for empirical validation. Specifically, we capture the significance of each event by Transformer, and combine it with the identified key event elements, to enhance the discriminative ability of the learned text embedding that summarizes all the events. Next, we produce the image embedding by combining the event tag jointly shared by the text and image with the visual embedding of the event-related image regions, which describes the eventual consequence of all the events and facilitates the event-based cross-modal alignment. Finally, we integrate text embedding and image embedding with the loss optimization empowered with the event tag by iteratively regulating the joint embedding learning for cross-modal retrieval. Extensive experiments demonstrate that our proposed event-oriented modality alignment approach significantly outperforms the state-of-the-art approach with a 23.3% improvement on top-1 Recall for image-to-recipe retrieval on Recipe1M 10k test set.

Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways generating contrasting pairs focus on augmenting or perturbating graph structures to obtain different views of the input graph. But such strategies could degrade the performances via adding noise into the graph, which may narrow down the field of the applications of graph contrastive learning. In this paper, we propose a novel graph contrastive learning method, namely Dual Space Graph Contrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. Since both spaces have their own advantages to represent graph data in the embedding spaces, we hope to utilize graph contrastive learning to bridge the spaces and leverage advantages from both sides. The comparison experiment results show that DSGC achieves competitive or better performances among all the datasets. In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.

This article introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding model. We use the Recipe1M dataset for the technical description and empirical validation. In preprocessing, we perform deep feature engineering by combining deep feature engineering with semantic context features derived from raw text-image input data. We leverage LSTM to identify key terms, deep NLP models from the BERT family, TextRank, or TF-IDF to produce ranking scores for key terms before generating the vector representation for each key term by using Word2vec. We leverage Wide ResNet50 and Word2vec to extract and encode the image category semantics of food images to help semantic alignment of the learned recipe and image embeddings in the joint latent space. In joint embedding learning, we perform deep feature engineering by optimizing the batch-hard triplet loss function with soft-margin and double negative sampling, taking into account also the category-based alignment loss and discriminator-based alignment loss. Extensive experiments demonstrate that our SEJE approach with deep feature engineering significantly outperforms the state-of-the-art approaches.

Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender systems. However, compared with traditional graph-based methods, the constructed hypergraphs are usually much sparser, which leads to a dilemma when balancing the benefits of hypergraphs and the modelling difficulty. Moreover, existing sequential hypergraph recommendation overlooks the temporal modelling among user relationships, which neglects rich social signals from the recommendation data. To tackle the above shortcomings of the existing hypergraph-based sequential recommendations, we propose a novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation (H2SeqRec) with the pre-training phase. Specifically, we design three self-supervised tasks to obtain the pre-training item embeddings to feed or fuse into the following recommendation architecture (with two ways to use the pre-trained embeddings). In the recommendation phase, we learn multi-scale item embeddings via a hierarchical structure to capture multiple time-span information. To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings. Also, we design an item enhancement module to capture dynamic social information at each timestamp to improve effectiveness. Extensive experiments are conducted on two real-world datasets to prove the effectiveness and high performance of the model.

Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets.

Cross-modal retrieval technology can help people quickly achieve mutual information between cooking recipes and food images. Both the embeddings of the image and the recipe consist of multiple representation subspaces. We argue that multiple aspects in the recipe are related to multiple regions in the food image. It is challenging to improve the cross-modal retrieval quality by making full use of the implicit connection between multiple subspaces of recipes and images. In this paper, we propose a multi-subspace implicit alignment cross-modal retrieval framework of recipes and images. Our framework learns multi-subspace information about cooking recipes and food images with multi-head attention networks; the implicit alignment at the subspace level promotes narrowing the semantic gap between recipe embeddings and food image embeddings; triple loss and adversarial loss are combined to help our framework for cross-modal learning. The experimental results show that our framework significantly outperforms to state-of-the-art methods in terms of MedR and [email protected] on Recipe 1M.

This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. The first tier improves recipe text embedding by optimizing the LSTM networks with term extraction and ranking enhanced sequence patterns, and optimizes the image embedding by combining the ResNeXt-101 image encoder with the category embedding using wideResNet-50 with word2vec. The second tier modality alignment optimizes the textual-visual joint embedding loss function using a double batch-hard triplet loss with soft-margin optimization. The third modality alignment incorporates two types of cross-modality alignments as the auxiliary loss regularizations to further reduce the alignment errors in the joint learning of the two modality-specific embedding functions. The category-based cross-modal alignment aims to align the image category with the recipe category as a loss regularization to the joint embedding. The cross-modal discriminator-based alignment aims to add the visual-textual embedding distribution alignment to further regularize the joint embedding loss. Extensive experiments with the one-million recipes benchmark dataset Recipe1M demonstrate that the proposed JEMA approach outperforms the state-of-the-art cross-modal embedding methods for both image-to-recipe and recipe-to-image retrievals.

User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via leveraging the advantages of graph neural networks (GNNs) or multi-task learning. However, most existing works do not take the complex dependencies among different behaviors of users into consideration. They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user’s behavioral patterns which will be used in downstream recommendation tasks. To tackle the challenge, in this paper, we first propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. How to obtain a unified embedding for a user from hyper meta-paths and avoid the previously mentioned limitations simultaneously is critical. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors. A new graph contrastive learning based framework is proposed by coupling with hyper meta-paths, namely HMG-CR, which consistently and significantly outperforms all baselines in extensive comparison experiments.

Person re-identification (re-id) has received ever-increasing research focus, because of its important role in video surveillance applications. This paper addresses the re-id problem between visible images of color cameras and infrared images of infrared cameras, which is significant in case that the appearance information is insufficient in poor illumination conditions. In this field, there are two key challenges, i.e., the difficulty to locate the discriminative information to re-identify the same person between visible and infrared images, and the difficulty to learn a robust metric for such large-scale cross-modality retrieval. In this paper, we propose a novel human body parts assistance network (BANet) to tackle the two challenges above. BANet mainly focuses on extracting discriminative information and learning robust features by leveraging the human body part cues. Extensive experiments demonstrate that the proposed approach outperforms the baseline and the state-of-the-art methods.

With the prevalence and rapid proliferation of SNS, dissemination of false information has become a big problem. In this paper, targeting Twitter, we propose a two-step approach for early detection of false information based on machine learning, which considers the event occurrence scale and the time series of tweets that compose the event. In Step 1, in the early stage of an event, whether it is false or true is decided if the prediction probability is high enough. In Step 2, the events whose authenticity cannot be determined in Step 1 are targeted for tracking, and their authenticity is ascertained as the tweets related to the events increase gradually. The experimental results comparing five machine learning models show that SVM is the optimal model for both steps and that our approach can achieve early detection of false information.

Recent advancements in convolutional neural networks based object detection have enabled analyzing the mounting video data with high accuracy. However, inference speed is a major drawback of these video analysis system because of the heavy object detectors. To address the computational and practicability challenges of video analysis, we propose FastQ, a system for efficient querying over video at scale. Given a target video, FastQ can automatically label the category and number of objects for each frame. We introduce a novel lightweight object detector named FDet to improve the efficiency of query system. First, a difference detector filters the frames whose difference is less than the threshold. Second, FDet is employed to efficiently label the remaining frames. To reduce inference time, FDet detects a center keypoint and a pair of corners from the feature map generated by a lightweight backbone to predict the bounding boxes. FDet completely avoid the complicated computation related to anchor boxes. Compared with state-of-the-art real-time detectors, FDet achieves superior performance with 29.1% AP on COCO benchmark at 25.3ms. Experiments show that FastQ achieves 150 times to 300 times speed-ups while maintaining more than 90% accuracy in video queries.

In recent years, the application of machine translation has become more and more widely. Currently, the neural multimodal translation models have made attractive progress, which combines images into deep learning networks, such as Transformer and RNN. When considering images in translation models, they directly apply gate structure or image attention to introduce image feature to enhance the translation effect. We argue that it may mismatch the text and image features since they are in different semantic space. In this paper, we propose a coordinated representation learning enhanced multimodal machine translation approach with multimodal attention. Our approach accepts the text data and its relevant image data as the input. The image features are fed into the decoder side of the basic Transformer model. Moreover, the Coordinated Representation Learning is utilized to map the different text and image modal features into their semantic representations. The mapped representations are linearly related in a shared semantic space. Finally, the sum of the image and text representations, called Coordinated Visual-Semantic Representation (CVSR), will be sent to a Multimodal Attention Layer (MAL) in our Transformer based translation approach. Experimental results show that our approach achieves the state-of-art performance on the public Multi30k dataset.

In recent years, people are facing with billions of food images, videos and recipes on social medias. An appropriate technology is highly desired to retrieve accurate contents across food images and cooking recipes, like cross-modal retrieval framework. Based on our observations, the order of sequential sentences in recipes and the noises in food images will affect retrieval results. We take into account the sentence-level sequential orders of instructions and ingredients in recipes, and noise portion in food images to propose a new framework for cross-retrieval. In our framework, we propose three new strategies to improve the retrieval accuracy. (1) We encode recipe titles, ingredients, instructions in sentence level, and adopt three attention networks on multi-layer hidden state features separately to capture more semantic information. (2) We apply attention mechanism to select effective features from food images incorporating with recipe embeddings, and adopt an adversarial learning strategy to enhance modality alignment. (3) We design a new triplet loss scheme with an effective sampling strategy to reduce the noise impact on retrieval results. The experimental results show that our framework clearly outperforms the state-of-art methods in terms of median rank and recall rate at top k on the Recipe 1M dataset.

Spatial database queries are often exploratory. The users often find that their queries return too many answers and many of them may be irrelevant. Based on the coupling relationships between spatial objects, this paper proposes a novel categorization approach which consists of two steps. The first step analyzes the spatial object coupling relationship by considering the location proximity and semantic similarity between spatial objects, and then a set of clusters over the spatial objects can be generated, where each cluster represents one type of user need. When a user issues a spatial query, the second step presents to the user a category tree which is generated by using modified C4.5 decision tree algorithm over the clusters such that the user can easily select the subset of query results matching his/her needs by exploring the labels assigned on intermediate nodes of the tree. The experiments demonstrate that our spatial object clustering method can efficiently capture both the semantic and location correlations between spatial objects. The effectiveness and efficiency of the categorization algorithm is also demonstrated.

With the pervasive use of GPS-enabled smart phones, location-based services, e.g., Location Based Social Networking (LBSN) have emerged . Point-of-Interests (POIs) Recommendation, as a typical component in LBSN, provides additional values to both customers and merchants in terms of user experience and business turnover. Existing POI recommendation systems mainly adopt Collaborative Filtering (CF), which only exploits user given ratings (i.e., user overall evaluation) about a merchant while regardless of the user preference difference across multiple aspects, which exists commonly in real scenarios. Meanwhile, besides ratings, most LBSNs also provide the review function to allow customers to give their opinions when dealing with merchants, which is often overlooked in these recommender systems. In this demo, we present MARS, a novel POI recommender system based on multi-aspect user preference learning from reviews by using utility theory. We first introduce the organization of our system, and then show how the user preferences across multiple aspects are integrated into our system alongside several case studies of mining user preference and POI recommendations.

With the pervasive use of mobile devices, Location Based Social Networks(LBSNs) have emerged in past years. These LBSNs, allowing their users to share personal experiences and opinions on visited merchants, have very rich and useful information which enables a new breed of location-based services, namely, Merchant Recommendation. Existing techniques for merchant recommendation simply treat each merchant as an item and apply conventional recommendation algorithms, e.g., Collaborative Filtering, to recommend merchants to a target user. However, they do not differentiate the user's real preferences on various aspects, and thus can only achieve limited success. In this paper, we aim to address this problem by utilizing and analyzing user reviews to discover user preferences in different aspects. Following the intuition that a user rating represents a personalized rational choice, we propose a novel utility-based approach by combining collaborative and individual views to estimate user preference (i.e., rating). An optimization algorithm based on a Gaussian model is developed to train our merchant recommendation approach. Lastly we evaluate the proposed approach in terms of effectiveness, efficiency and cold-start using two real-world datasets. The experimental results show that our approach outperforms the state-of-the-art methods. Meanwhile, a real mobile application is implemented to demonstrate the practicability of our method.

Nowadays, a huge amount of text is being generated for social networking purpose on the Web. Keyword extraction from such text benefit many applications such as advertising, search, and content filtering. Recent studies show that graph based ranking is more effective than traditional term or document frequecy based approaches. However, most work in the literature constructs word to word graph within a document or a collection of documents before applying a kind of random walk. Such a graph does not consider the influence of document importance on keyword extraction. Moreover, social text like a microblog post usually has speical social features such as hashtag and so on, which can help us understand its topic. In this paper, we propose hashtag biased ranking for keyword extraction from a collection of microblog posts. We first build a word-post weighted graph by taking into account the posts themselves. Then, a hashtag biased random walk is applied on this graph, which guides our approach to extract keywords according to the hashtag topic. Last, the final ranking of a word is determined by the stationary probability after a number of interations. We evaluate our proposed method on a real Chinese microblog posts. Experiments show that our method is more effective than the traditional word to word graph based ranking in terms of precision.

In recent years, there has been a prevalence of search engines being employed to find useful information in the Web as they efficiently explore hyperlinks between web pages which define a natural graph structure that yields a good ranking. Unfortunately, current search engines cannot effectively rank those relational data, which exists on dynamic websites supported by online databases. In this study, to rank such structured data (i.e., find the "best" items), we propose an integrated online system consisting of compressed data structure to encode the dominant relationship of the relational data. Efficient querying strategies and updating scheme are devised to facilitate the ranking process. Extensive experiments illustrate the effectiveness and efficiency of our methods. As such, we believe the work in this poster can be complementary to traditional search engines.