The proliferation of collaborative training and multi-person sports has underscored the necessity for concurrent whole-field action sensing. However, Electromyography (EMG) recognition, which plays a pivotal role in Wearable Human Activity Recognition (WHAR) for analyzing muscle activity and decoding action intent, still faces challenges in achieving a balance between performance, cost, and efficiency in multi-person scenarios. Unlike current channel-expansion solutions, we propose a wireless wearable Single-Dimensional Sparse EMG (2SEMG) Sensor for efficient personal sampling. These action-unaffected sensors leverage the proposed lightweight One-Dimensional Motion Network (OMONet) to facilitate concurrent action sensing. Experiments demonstrate that OMONet achieves leading performance and efficiency in action signal recognition, and two real-world badminton matches further confirm the performance, robustness, and real-time efficiency of the whole-field action sensing network constructed via 2SEMG Sensors and OMONet.

Existing multimedia recommender systems provide users with suggestions of media by evaluating similarities, such as games and movies. To enhance the semantics and explainability of embeddings, it is a consensus to apply additional information (e.g., interactions, contexts, popularity). However, without systematically considering representativeness and value, the utility and explainability of embedding drop drastically. Hence, we introduceRVRec, a plug-and-play model-agnostic embedding enhancement approach that can improve both personality and explainability of existing systems. Specifically, we propose a probability-based embedding optimization method that uses a contrastive loss based on negative 2-Wasserstein distance to learn to enhance the representativeness of the embeddings. In addition, we introduce a reweighing method based on a multivariate Shapley values strategy to evaluate and explore the value of interactions and embeddings. Extensive experiments on multiple backbone recommenders and real-world datasets show that RVRec can improve the personalization and explainability of existing recommenders, outperforming state-of-the-art baselines.

Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, known as “gray failure”, for AI workloads, significantly affecting end-to-end performance and concealing performance issues, which complicates root cause analysis for failures and regressions. We introduce SuperBench, a proactive validation system for AI infrastructure that mitigates hidden degradation caused by hardware redundancies and enhances overall reliability. SuperBench features a comprehensive benchmark suite, capable of evaluating individual hardware components and representing most real AI workloads. It comprises a Validator that learns benchmark criteria to pinpoint defective components clearly. Additionally, SuperBench incorporates a Selector to balance validation time and issue-related penalties, enabling optimal timing for validation execution with a tailored subset of benchmarks. Through testbed evaluation and simulation, we demonstrate that SuperBench can increase the mean time between incidents by up to 22.61×. SuperBench has been successfully deployed in Azure production, validating hundreds of thousands of GPUs every year.

Audio classification plays a crucial role within fields such as human-machine interaction and intelligent robotics. However, high-performance audio classification systems typically demand significant computational and storage resources, posing substantial challenges when deploying to the resource-constrained edge devices with an urgent need for such capabilities. To achieve a new level of balance between model complexity and performance, we introduce a novel multi-view method for the separated time-frequency features extraction and utilization, which exists within the proposed Mini Mirror Multi-View Network (M3Net) in the form of the Mirror Attention mechanism. M3Net enables reversible spatial transformation of spectral features is capable of efficiently leverages robust local and global features in the time and frequency domains with low requirements for parameters. Experiments based on Mel-Spectrogram without data augmentation and pre-training indicate that M3Net can achieve classification accuracy over 97% on the UrbanSound8K and SpeechCommandsV2 datasets with only 0.03 million parameters. The contribution of each functional segment in M3Net is fully verified and explained in the ablation experiments.

The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to significant quantization errors and limited representational capacity. This work introduces the first FP4 training framework for LLMs, addressing these challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse. To ensure stability, the framework integrates a mixed-precision training scheme and vector-wise quantization. Experimental results demonstrate that our FP4 framework achieves accuracy comparable to BF16 and FP8, with minimal degradation, scaling effectively to 13B-parameter LLMs trained on up to 100B tokens. With the emergence of next-generation hardware supporting FP4, our framework sets a foundation for efficient ultra-low precision training.

Conversational Recommendation Systems (CRS) play a pivotal role in personalized cultural discovery by guiding user attention and mitigating information overload through interactive dialogue. However, existing Transformer-based CRS models predominantly focus on token-level generation, limiting their ability to capture sentence-level semantic interaction patterns. Furthermore, while user preferences are often inferred through interaction entities among these entities, current approaches typically overlook the sequential dependencies, either semantic or ID-based, which are crucial for accurate and context-aware recommendations. To overcome these limitations, we propose SDCRS, a sequence-augmented CRS based on diffusion models, which integrates sentence-level and entity-level sequential modeling to enhance the response generation and recommendation modules. By integrating diffusion mechanisms, SDCRS not only improves the diversity of generated responses but also enhances user preference modeling, particularly under cold-start conditions. Comprehensive experiments clearly demonstrate that SDCRS achieves superior performance over all baselines.

Oriented object detection seeks to determine both the position and orientation of objects, yet angle periodicity often limits performance. To solve this issue, we rethink label assignment and sampling strategies and propose a pair of orientation-aware assigner and sampler (OAS) for a two-stage detector. The orientation-aware assigner (OA) incorporates angle and location to improve positive and negative sample assignment, while the orientation-aware sampler (OS) ranks positive samples by their angular difference from ground truth, adjusting learning weights by soft sampling. Such a design significantly mitigates the angle periodicity problem and enables detector focusing on high-quality samples with a more consistent orientation for training. Experimental results on two challenging oriented object detection benchmarks demonstrate that OAS can consistently boost the detection accuracy based on many existing two-stage detectors (e.g., Oriented R-CNN and RPGAOD) without additional cost. Both code and pretrained models are available athttps://github.com/skyandkibo/OAS.

Deep neural network advancements have led to significant progress in industrial applications, particularly in product recognition for smart automated vending machines (AVMs). This area has seen increased market demand as a fundamental part of automatedretail. However, the existing works possess critical gaps: 1) densely placed and obscured objects in AVMs lead to inaccurate product recognition results, necessitating auxiliary information for achieving precise detection; 2) the lack of datasets with auxiliary information hinders further development in this field. To address these gaps, we propose a depth-guided product recognition network, which consists of two novel components: a depth-aware feature pyramid network (DFPN) and a depth-aware regression head (DRH). Our DFPN can adaptively select features that are beneficial for regression from both red, green, blue (RGB) and depth data, whereas the DRH refines the regression branch via depth information without affecting the classification process. In addition, to overcome dataset limitations, we develop an extended and fully annotated depth information dataset namedSmartUVM-D, which includes depth information for each image based on the existing SmartUVM dataset. The experimental results obtained on our SmartUVM-D benchmark show that our method effectively solves the inaccurate product recognition problem and achieves substantial gains over the baseline approaches. Specifically, our method (based on the ATSS framework) achieves a mean average precision of 84.4, representing a 2.3-point improvement over the previously developed ATSS method and establishing a new state-of-the-art approach.

Improving model prediction capability based on continuously collected data is one of the biggest challenges in a mature, intelligent weather forecasting system. To tackle this problem, we propose an online incremental learning strategy for meteorological models with asynchronous updates. In short, we divide two different meteorological incremental learning settings according to the characteristics of meteorological data, distinguishing between increments within short-term time windows and increments for long-term data. For short-term data, a structure-based incremental learning method with a fast iteration rate is used, and a Residual-Net (R-Net) is proposed to improve the performance of the model; for long-term data, a replay-based incremental learning method called Gradient-based Core-set Selection and Weighting method (GCSW) is proposed, by performing cosine distance and normalization calculations to obtain sample weights and weighting the coreset samples to avoid catastrophic forgetting. The comparative experiments on temperature prediction incremental datasets in Beijing and Xi’an show that our proposed method achieves the best results in both short-term and long-term incremental experimental settings than all other baseline methods, demonstrating the advantages of the algorithm we proposed on meteorological datasets. The code is available as an open source repository on GitHub https://github.com/liujunjiao1/Enhancing-Weather-Model.

Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The scarcity of new training data will seriously destroy the model’s stability and plasticity. Continually Evolved Classifiers (CEC) (Zhang et al., 2021), a kind of framework, maintains the stability by freezing the encoder and achieves the plasticity by evolving the classifier along with a pseudo incremental learning scheme. However, the performance of CEC is limited due to 1) inequitable information gains between classifier weights and test features, and 2) inefficient learning task construction strategy. To address the first issue, we propose a Knowledge-guided Relation Refinement Module (KRRM) to update both the classifier weights and test features. The main function of KRRM is achieved through cross-attention to propagate the knowledge represented by old encoded data. To address the second issue, we design a Pseudo Incremental relation Refinement Learning (PIRL) that utilizes a novel hard concepts mining strategy to mine hard concept tasks globally and locally. By successfully addressing the two issues, our proposed method, named Improved Continually Evolved Classifiers (CEC+), extends the potential of CEC without introducing any additional parameters. More precisely, extensive experiments on CIFAR100, miniImageNet, and Caltech-UCSD Birds-200-2011, demonstrate that our proposed method surpasses prior state-of-the-art methods.

AI-based methods are shining across a variety of industries, especially unmanned retail. Product recognition is the problem of recognizing the category and quantity of products (e.g., beverages and mineral water) in intelligent unmanned vending machines (UVMs) to automatic checkout during purchase. However, for similar products in hundreds of categories, the existing method is not accurate enough. Besides, they cannot be extended for new products without retraining. In this article, we propose a product recognition approach based on intelligent UVMs, calledSplit-Check, which first splits the region of interest of products by detection and then check product by instance-level retrieval. Split-Check is the combination of two important components. The preliminary detection distinguishes items that contain the different coarse-grained features, then locates items, and classifies them into coarse-grained categories as a candidate. The retrieval further distinguishes the candidate items that contain the different fine-grained features. Besides, we reconstruct a large-scale categories product dataset GOODS-85 based on actual UVMs scenarios, in which the number of categories of items is larger than the existing dataset. Experimental results demonstrate the effectiveness of the proposed approach. Our method significantly improves the recognition performance of hundreds of products and increases the scalability of products.

Conversational recommendation systems (CRS) can actively discover users’ preferences and perform recommendations during conversations. The majority of works on CRS tend to focus on a single conversation and dig it using knowledge graphs, language models, etc. However, they often overlook the abundant and rich preference information that exists in the user's historical conversations. Meanwhile, end-to-end generation of recommendation results may lead to a decrease in recommendation quality. In this work, we propose a personalized conversational recommendation system infused with historical interaction information. This framework leverages users’ preferences extracted from their historical conversations and integrates them with the users’ preferences in current conversations. We find that this contributes to higher accuracy in recommendations and fewer recommendation turns. Moreover, we improve the interactive pattern between the recommendation module and the dialogue generation module by utilizing the slot filling method. This enables the results inferred by the recommendation module to be integrated into the conversation naturally and accurately. Our experiments on the benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art methods in the evaluation of recommendations and dialogue generation.

Sequential recommendation mines the user's interaction sequence or time information to get better recommendations and thus is gaining more and more attention. Existing sequential recommendations tend to build new models, and the study of the loss function is seriously neglected. Despite the increasing attention paid to contrastive learning recently, we believe that the key to contrastive learning is contrastive loss(CL), which also provides a new option for sequential recommendation. However, we find it works against the personalized representation of features. First, it is a relative constraint that keeps positive and negative samples away from each other but without an absolute constraint. Second, recent studies have shown that all embeddings should be uniformly distributed. However, CL only widens the distance of positive and negative samples within the training batch, rather than making a uniform distribution of all items. These two shortcomings make the embedding space too compact, which is harmful to personalized representation and recommendation. Therefore, this article proposes Personalized Contrastive Loss (PCL) to combine CL with absolute constraints of BCE/CE and employs regularization methods to make the representations uniformly distributed. State-of-the-art results are obtained in experiments on several commonly used datasets. The code and data will be available on GitHub.

Existing point-of-interest (POI) recommendation methods only show the direct recommendation results and lack the proper reasons for recommendation. In recent years, explainable recommendation has become an increasingly important subfield in recommendation systems. The aim of explainable recommendation is to provide a reason why an item is recommended to a user. In this way, it helps to improve the transparency, persuasiveness and user satisfaction of recommendation systems. The explainable recommendation should indicate users' preferences for POIs, such as the category and the price. In addition, to increase the diversity of the results, we take emotional intensity into account in our model to generate more vivid reasons. To this end, we propose a hierarchical attention-based transformer model to generate reasons with specific topics and different emotions. With a hierarchical attention mechanism, we can capture the word-level and attribute-level preferences of users. In addition, we also learn the latent representation of the emotion score to generate diverse recommendation reasons. We evaluate the proposed model on a new real-world dataset collected from three travel service websites. The experimental results demonstrate that our method outperforms the related approaches for reason generation.

Practical applications with visual question answering (VQA) systems are challenging, and recent research has aimed at investigating this important field. Many issues related to real-world VQA applications must be considered. Although existing methods have focused on adding external knowledge and other descriptive information to assist in reasoning, they are limited by the impact of information retrieval errors on downstream tasks and the misalignment of the aggregated information. Thus, the overall performance of these models must be improved. To address these challenges, we propose a novel VQA model that utilizes a differentiated pretrained model to represent the input information and connects the input data with three external knowledge components through a common feature space. To combine the information in the three feature spaces, we propose an information aggregation strategy that employs a weighted score to aggregate the information in the relation and entity spaces in the answer prediction process. The experimental results show that our method achieves good performance in fact-based and zero-shot VQA tasks and achieves state-of-the-art performance with the ZS-F-VQA dataset.

Cross-Domain Recommendation (CDR) aims to alleviate the cold-start problem by transferring knowledge from a data-rich domain (source domain) to a data-sparse domain (target domain), where knowledge needs to be transferred through a bridge connecting the two domains. Therefore, constructing a bridge connecting the two domains is fundamental for enabling cross-domain recommendation. However, existing CDR methods often overlook the valuable of natural relationships between items in connecting the two domains. To address this issue, we propose DKTCDR: a Domain-oriented Knowledge Transfer method for Cross-Domain Recommendation. In DKTCDR, We leverages the rich relationships between items in a cross-domain knowledge graph as bridges to facilitate both intra- and inter-domain knowledge transfer. Additionally, we design a cross-domain knowledge transfer strategy to enhance inter-domain knowledge transfer. Furthermore, we integrate the semantic modality information of items with the knowledge graph modality information to enhance item modeling. To support our investigation, we construct two high-quality cross-domain recommendation datasets, each containing a cross-domain knowledge graph. Our experimental results on these datasets validate the effectiveness of our proposed method. Source code is available athttps://github.com/zxxxl123/DKTCDR.

Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categories product recognition based on intelligent UVMs. It can be divided into two parts: 1) first, we explore the similarities and differences between products through manifold learning, and then we build a hierarchical multigranularity label to constrain the learning of representation; and 2) second, we propose a hierarchical label object detection network, which mainly includes coarse-to-fine refine module (C2FRM) and multiple granularity hierarchical loss (MGHL), which are used to assist in capturing multigranularity features. The highlights of our method are mine potential similarity between large-scale category products and optimization through hierarchical multigranularity labels. Besides, we collected a large-scale product recognition dataset GOODS-85 based on the actual UVMs scenario. Experimental results and analysis demonstrate the effectiveness of the proposed product recognition methods.

The high rate of false arrhythmia alarms in intensive care units (ICUs) can negatively impact patient care and lead to slow staff response time due to alarm fatigue. To reduce false alarms in ICUs, previous works proposed conventional supervised learning methods which have inherent limitations in dealing with high-dimensional, sparse, unbalanced, and limited data. We propose a deep generative approach based on the conditional denoising diffusion model to detect false arrhythmia alarms in the ICUs. Conditioning on past waveform data of a patient, our approach generates waveform predictions of the patient during an actual arrhythmia event, and uses the distance between the generated and the observed samples to classify the alarm. We design a network with residual links and self-attention mechanism to capture long-term dependencies in signal sequences, and leverage the contrastive learning mechanism to maximize distances between true and false arrhythmia alarms. We demonstrate the effectiveness of our approach on the MIMIC II arrhythmia dataset for detecting false alarms in both retrospective and real-time settings.

Short-to-medium term temperature prediction in high resolution is a very challenging task, involving meteorology, physics, mathematics, geography, and many other subjects. Its purpose is to fit a complex function from historical meteorological data to predict the future 1–5 days temperature, which is a typical spatio-temporal prediction problem. Meteorological data show complex correlations in local space. Most of the existing machine learning methods are based on image pixel-level tasks or spatio-temporal prediction tasks, which model meteorological data without considering the characteristics of meteorological data and use rough global patterns to model local space which would lose many details. To address the above issues, our work fine-grained conditional convolution network (FCCN) proposes a novel grid-level conditional convolution module, including a local geographic adaptive weight (GAW) and a local data adaptive weight (DAW). These two components are integrated into a multiscale meteorological fusion gated recurrent unit (GRU) architecture for the end-to-end temperature prediction. Experiments in real-world datasets from ERA-5 show our FCCN model has a better performance than all other baseline methods.

Dating recommendation becomes a critical task since the rapid development of online dating sites and it is beneficial for users to find their ideal relationships from a large number of registered members. Different users usually have different tastes when choosing their dating partners. Therefore, it is necessary to distinguish the users personal features and preferences in dating recommendation methods. However, present approaches dont capture enough user preferences from social graph and attribute data. They also ignore user attributes, which is the complementary and consistent side information of user social graphs. In this paper, we propose a Matching Individual Preferences with Features (MIPF) model to recommend dating partners jointly using user attributes and social graphs. We aim to model user features and preferences to identify what the user has and what the user likes. We also distinguish user preferences into explicit preferences and implicit preferences. The implicit preferences are mined from social graphs, while the explicit preferences are captured from the social links. Additionally, convolutional neural networks are used to extract the latent non-linear information in user attributes. Experiments on real-world online dating datasets demonstrate our MIPF model is superior to existing methods.

Most recommendation systems focus on predicting rating or finding aspect information in reviews to understand user preferences and item properties. However, these methods ignore the effectiveness and persuasiveness of recommendation results. Consequently, explainable recommendation, namely providing recommendation results with recommendation reasons at the same time, has attracted increasing attention of researchers due to its ability in fostering transparency and trust. It is lucky that some E-commerce websites provide a kind of new interaction box called Tips and users can express their comments on items with a simple sentence. This brings us an opportunity to realize explainable recommendation. Under the supervision of two explicit feedback, namely rating and textual tips, we can implement a multi-task learning model which can provide recommendation results and generate recommendation reasons at the same time. In this paper, we propose an Encoder-Decoder and Multi-Layer Perception (MLP) based Explainable Recommendation model named EMER to simultaneously implement reason generation and rating prediction. Items title contains significant product-related information and plays an important role in grabbing users attention, so we fuse it in our model to generate recommendation reasons. Numerous experiments on benchmark datasets demonstrate that our model is superior to the state-of-the-art models.

Top-k recommendation is a fundamental task in recommendation systems that is generally learned by comparing positive and negative pairs. The contrastive loss (CL) is the key in contrastive learning that has recently received more attention, and we find that it is well suited for top-k recommendations. However, CL is problematic because it treats the importance of the positive and negative samples the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, there are so few positive items in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. Consequently, we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a multisample-based contrastive loss (MSCL) function that solves the two problems by balancing the importance of positive and negative samples and data augmentation. Based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. Our code is available athttps://github.com/haotangxjtu/MSCL.

Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users' dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and conversation flow hinders their practicality and utility. In reality, there exist various relationships among slots and values, while users' requirements may dynamically adjust or change. Moreover, the conversation often involves visual modality to facilitate the conversation. These actually call for a more advanced internal state representation of the dialogue and a proper reasoning scheme to guide the decision making process. In this paper, we explore multiple facets of multimodal conversational recommendation and try to address the above mentioned challenges. In particular, we represent the structured back-end database as a multimodal knowledge graph which captures the various relations and evidence in different modalities. The user preferences expressed via conversation utterances will then be gradually updated to the state graph with clear polarity. Based on these, we train an end-to-end State Graph-based Reasoning model SGR to perform reasoning over the whole state graph. The prediction of our proposed model benefits from the structure of the graph. It not only allows for zero-shot reasoning for items unseen in training conversations, but also provides a natural way to explain the policies. Extensive experiments show that our model achieves better performance compared with existing methods.

Dialogue state is a key information in traditional task-oriented dialogue systems, which represents the user's dialogue intention at each moment through a set of (slot, value). The recent methods model the slot and the dialogue context to keep track of the state, but there is a lack of refinement of context information. They do not consider the influence of dialogue context in different scenarios. Our proposed approach utilizes a fine-grained representation of each slot at multiple levels and incorporates an interaction mechanism to obtain a weight of past memory, present utterance and relevance of the slots. Besides, to address the problem that the dialogue utterance is semantically distant from the corresponding slot value, we introduce the contrastive learning to make the utterance embedding mapped under each slot name more suitable with the ground truth value and away from other slot values. This improves the accuracy of mapping between feature space and semantic space. In the predefined ontology-based approaches, our model achieves leading results with both MultiWOZ2.0 and MultiWOZ2.1 datasets.

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In this paper, we aim to show that an end-to-end deep neural network unifying training and indexing stages can significantly improve the recall performance of traditional methods. To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query. To optimize the recall performance of NCI, we invent a prefix-aware weight-adaptive decoder architecture, and leverage tailored techniques including query generation, semantic document identifiers, and consistency-based regularization. Empirical studies demonstrated the superiority of NCI on two commonly used academic benchmarks, achieving +21.4% and +16.8% relative enhancement for Recall@1 on NQ320k dataset and R-Precision on TriviaQA dataset, respectively, compared to the best baseline method.

Emoji recommendation is an important task to help users find appropriate emojis from thousands of candidates based on a short tweet text. Traditional emoji recommendation methods lack personalized recommendation and ignore user historical information in selecting emojis. In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. In text encoder, a BERT model is contained to learn dense and low-dimensional representations of tweets. In personalized attention, user dynamic preferences are learned according to semantic and sentimental similarity between historical tweets and the tweet which is waiting for emoji recommendation. Informative historical tweets are selected and highlighted. Experiments are carried out on two real-world datasets from Sina Weibo and Twitter. Experimental results validate the superiority of our approach on personalized emoji recommendation.

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users’ historical check-in data. Therefore, it is crucial to model both users’ general taste and recent sequential behaviors. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for different users. Besides, the locations and categories of POIs contain different information about users’ preference. However, current researchers always treat them as the same factors or believe that categories determine where to go. To this end, we propose a novel method named Personalized Long- and Short-term Preference Learning (PLSPL) to learn the specific preference for each user. Specially, we combine the long- and short-term preference via user-based linear combination unit to learn the personalized weights on different parts for different users. Besides, the context information such as the category and check-in time is also essential to capture users’ preference. Therefore, in long-term module, we consider the contextual features of POIs in users’ history records and leverage attention mechanism to capture users’ preference. In the short-term module, to better learn the different influences of locations and categories of POIs, we train two LSTM models for location- and category-based sequence, respectively. Then we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.

Sequential recommendations aim to predict the user’s next behaviors items based on their successive historical behaviors sequence. It has been widely applied in lots of online services. However, current sequential recommendations use the adjacent behaviors to capture the features of the sequence, ignoring the features among nonadjacent sequential items and the summarized features of the sequence. To address the above problems, in this paper, we propose an annular-graph attention based sequential recommendation (AGSR) model by exploring user’s long-term and short-term preferences for the personalized sequential recommendation. For user’s short-term preferences, AGSR builds an annular-graph on the sequence of user behavior. Then, AGSR proposes an annular-graph attention applying on the sub annular-graph to explore local features and applying annular-graph attention on entire annular-graph to explore the global features and the skip features. For user’s long-term preferences, the latent factor model are introduced in AGSR. The experimental results on two public datasets show that our model outperforms the state-of-the-art methods.

With the popularity of social platforms, emoji appears and becomes extremely popular with a large number of users. It expresses more beyond plaintexts and makes the content more vivid. Using appropriate emojis in messages and microblog posts makes you lovely and friendly. Recently, emoji recommendation becomes a significant task since it is hard to choose the appropriate one from thousands of emoji candidates. In this paper, we propose a Context-Aware Personalized Emoji Recommendation (CAPER) model fusing the contextual information and the personal information. It is to learn latent factors of contextual and personal information through a score-ranking matrix factorization framework. The personal factors such as user preference, user gender, and the current time can make the recommended emojis meet users' individual needs. Moreover, we consider the co-occurrence factors of the emojis which could improve the recommendation accuracy. We conduct a series of experiments on the real-world datasets, and experiment results show better performance of our model than existing methods, demonstrating the effectiveness of the considering contextual and personal factors.

Deep multi-view subspace clustering has achieved promising performance compared with other multi-view clustering. However, existing deep multi-view subspace clustering only considers the global structure for all views, and they ignore the local geometric structure among each view. In addition, they cannot learn discriminative feature on different clusters of different views, i.e., inter-cluster difference. To solve these problems, in this paper, we propose a novel Deep Multi-view Subspace Clustering with Unified and Discriminative Learning (DMSC-UDL). DMSC-UDL combines global and local structures with self-expression layer. The global and local structures help each other forward and achieve small distance between samples of the same cluster. To make samples in different clusters of different views farther, DMSC-UDL uses a discriminative constraint between different views. In this way, DMSC-UDL makes the same cluster's samples have large weights, while different clusters' samples have small weights. Thus, it can learn a better shared connection matrix for multi-view clustering. Extensive experimental results reveal that the proposed multi-view clustering method is superior to several state-of-the-art multi-view clustering methods in terms of performance.

The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the higher storage requirement and computational cost of retraining previous tasks with MTMV models have presented a formidable challenge for this lifelong learning scenario. To address this challenge, in this article, we propose a new continual multiview task learning model that integrates deep matrix factorization and sparse subspace learning in a unified framework, which is termed deep continual multiview task learning (DCMvTL). More specifically, as a new multiview task arrives, DCMvTL first adopts a deep matrix factorization technique to capture hidden and hierarchical representations for this new coming multiview task while accumulating the fresh multiview knowledge in a layerwise manner. Then, a sparse subspace learning model is employed for the extracted factors at each layer and further reveals cross-view correlations via a self-expressive constraint. For model optimization, we derive a general multiview learning formulation when a new multiview task comes and apply an alternating minimization strategy to achieve lifelong learning. Extensive experiments on benchmark data sets demonstrate the effectiveness of our proposed DCMvTL model compared with the existing state-of-the-art MTMV and lifelong multiview task learning models.

Partial differential equations (PDEs) are essential foundations to model dynamic processes in natural sciences. Discovering the underlying PDEs of complex data collected from real world is key to understanding the dynamic processes of natural laws or behaviors. However, both the collected data and their partial derivatives are often corrupted by noise, especially from sparse outlying entries, due to measurement/process noise in the real-world applications. Our work is motivated by the observation that the underlying data modeled by PDEs are in fact often low rank. We thus develop a robust low-rank discovery framework to recover both the low-rank data and the sparse outlying entries by integrating double low-rank and sparse recoveries with a (group) sparse regression method, which is implemented as a minimization problem using mixed nuclear norms with ℓ1 and ℓ0 norms. We propose a low-rank sequential (grouped) threshold ridge regression algorithm to solve the minimization problem. Results from several experiments on seven canonical models (i.e., four PDEs and three parametric PDEs) verify that our framework outperforms the state-of-art sparse and group sparse regression methods. Code is available at https://github.com/junli2019/Robust-Discovery-of-PDEs

Effective location recommendation is an important problem in both research and industry. Much research has focused on personalized recommendation for users. However, there are more uses such as site selection for firms and factories. In this study, we try to solve site selection problem by recommending some locations satisfying special requirements. There are many factors affecting it, including functions of architecture, building cost, pollution discharge etc. We focus on the specific site selection of meteorological observation stations in this paper with leveraging the factors of functions of architecture and building cost from multi-source urban big data. We consider not only recommending the locations that can provide more accurate prediction and cover more areas, but also minimizing the cost of building new stations. We design an extensible two-stage framework for the station placing including prediction model and recommendation model. It is very convenient for executives to add more real-life factors into our approach. We have some empirical findings and evaluate the proposed approach using the real meteorological data of Shaanxi province, China. Experiment results show the better performance of our approach than existing commonly used methods.

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to capture user preference. To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information. In long-term module, we learn the contextual features of POIs and leverage attention mechanism to capture users' preference. In the short-term module, we utilize LSTM to learn the sequential behavior of users. Specifically, to better learn the different influence of location and category of POIs, we train two LSTM models for location-based sequence and category-based sequence, respectively. Then we combine the long and short-term results to recommend next POI for users. At last, we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as “Customers who bought this item also bought…”. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as “Campus radio plays this song at noon every day, and I think it sounds wonderful,” which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.

In recent years, we have witnessed a flourish of social review websites. Internet users can easily share their experiences on some products and services with their friends. Therefore, measuring interpersonal influence becomes a popular method for recommender systems. However, traditional works are all based on external tangible activities, such as following, retweeting, mentioning, etc. In this paper, we explore user internal factors to measure his/her influence on a specific domain, namely, the social network on local businesses. The proposed user internal factors include user sentimental deviations and the review's reliability. The internal factors are not from explicit behavior but could help us to understand users. In addition, we utilize an attention mechanism that could auto-learn the weights of different factors. Through a case study on the Yelp dataset, we found that the proposed user internal factors on influence, that is, the proposed user sentimental deviations and the review's reliability, are effective in improving the accuracy of rating predictions.

Recently, advances in intelligent mobile device and positioning techniques have fundamentally enhanced social networks, which allows users to share their experiences, reviews, ratings, photos, check-ins, etc. The geographical information located by smart phone bridges the gap between physical and digital worlds. Location data functions as the connection between user's physical behaviors and virtual social networks structured by the smart phone or web services. We refer to these social networks involving geographical information as location-based social networks (LBSNs). Such information brings opportunities and challenges for recommender systems to solve the cold start, sparsity problem of datasets and rating prediction. In this paper, we make full use of the mobile users' location sensitive characteristics to carry out rating prediction. We mine: 1) the relevance between user's ratings and user-item geographical location distances, called as user-item geographical connection, 2) the relevance between users' rating differences and user-user geographical location distances, called as user-user geographical connection. It is discovered that humans' rating behaviors are affected by geographical location significantly. Moreover, three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity, are fused into a unified rating prediction model. We conduct a series of experiments on a real social rating network dataset Yelp. Experimental results demonstrate that the proposed approach outperforms existing models.

Nowadays, with the boom of social media and e-commerce, more and more people prefer to share their consumption experiences and rate services on review sites. Much research has focused on personalized recommendation. However, quality of service also plays an important role in recommender systems, and it is the main concern of this paper. An overall rating that indicates the popular view usually represents the evaluation. There are some challenges when we do not have enough review information to extract public opinion. Take, for example, a movie for which one user rates a two star rating, and another rates a five star rating. In this case, it is difficult to conduct a quality evaluation fairly. However, it is possible to be improved with the help of big social users' contextual information. In this paper, we propose a model to conduct service quality evaluation by improving overall rating of services using an empirical methodology. We use the concept of user rating's confidence, which denotes the trustworthiness of user ratings. First, entropy is utilized to calculate user ratings' confidence. Second, we further explore spatial-temporal features and review sentimental features of user ratings to constrain their confidences. Last, we fuse them into a unified model to calculate an overall confidence, which is utilized to perform service quality evaluation. Extensive experiments implemented on Yelp and Douban Movie datasets demonstrate the effectiveness of our model.

In recent years, we have witnessed a flourish of review websites. It presents a great opportunity to share our viewpoints for various products we purchase. However, we face an information overloading problem. How to mine valuable information from reviews to understand a user's preferences and make an accurate recommendation is crucial. Traditional recommender systems (RS) consider some factors, such as user's purchase records, product category, and geographic location. In this work, we propose a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems. Firstly, we propose a social user sentimental measurement approach and calculate each user's sentiment on items/products. Secondly, we not only consider a user's own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, we consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers' comprehensive evaluation. At last, we fuse three factors-user sentiment similarity, interpersonal sentimental influence, and item's reputation similarity-into our recommender system to make an accurate rating prediction. We conduct a performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp. Our experimental results show the sentiment can well characterize user preferences, which helps to improve the recommendation performance.

With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users' rating behaviors. In order to predict user-service ratings, we focus on users' rating behaviors. In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user's rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users' daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users' rating behaviors. In the proposed user-service rating prediction approach, we fuse four factors-user personal interest (related to user and the item's topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users' rating behavior habits), and interpersonal rating behavior diffusion (related to users' behavior diffusions)-into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach.

With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on three rating datasets: Yelp, MovieLens, and Douban Movie. Experimental results show the proposed approach outperforms the existing RS approaches.