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Jun Yan

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19 papers

2 author rows

AAAI Conference 2026 Conference Paper

Reinforcement Learning with Fuzzy Human Attention-Guided Graph for Heterogeneous Multiagent Systems

Dingbang Liu
Fenghui Ren
Jun Yan
Guoxin Su
Shohei Kato
Wen Gu

Effective agent coordination is crucial in cooperative Multiagent Reinforcement Learning (MARL). While recent advances have significantly improved cooperation by modeling agent interactions through various graph structures, most existing approaches primarily focus on homogeneous agents. Despite the ubiquity of heterogeneous agents, constructing a comprehensive graph that captures their diverse attributes and relationships from scratch is notoriously labor-intensive for both humans and agents, which makes policy learning extremely challenging. To tackle this difficulty, we propose a novel method that utilizes a fuzzy human attention-guided graph to model inter-agent relationships. Instead of learning the graph entirely from scratch, we incorporate abstract human attention, with its uncertainty captured through fuzzy logic, to guide the graph development process. To further accommodate the varying attributes and objectives of heterogeneous agents while maintaining their learning capabilities, the attention-guided graph is fine-tuned through a hyper-network. Our proposed approach is end-to-end trainable and agnostic to specific MARL methods. Empirical evaluations conducted on challenging heterogeneous scenarios from the StarCraft Multiagent Challenge (SMAC) and SMACv2 validate the effectiveness of the proposed method.

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AAAI Conference 2026 Conference Paper

Towards Zero-Shot Diabetic Retinopathy Grading: Learning Generalized Knowledge via Prompt-Driven Matching and Emulating

Huan Wang
Haoran Li
Yuxin Lin
Huaming Chen
Jun Yan
Lijuan Wang
Jiahua Shi
Qihao Xu

As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fundus images can significantly enhance DR grading performance by expanding the field of view (FOV). However, there is a long-tailed distribution problem in fundus image analysis, i.e., a high prevalence of mild DR grades and a low prevalence of rare ones (e.g., cases of high severity), which presents a significant challenge to developing a unified model capable of detecting rare or unseen DR grades not encountered during training. In this paper, we propose ProME-DR, a Prompt-driven zero-shot DR grading framework, which leverages prompt Matching and Emulating to recognize the unseen DR categories and views beyond the training set. ProME-DR disentangles the training process into two stages to learn generalized knowledge for novel DR disease grading. Initially, ProME-DR leverages two sets of prompt units to capture semantic and inter-view consistency knowledge via a split-and-mask manner, gathering instance-level DR visual clues. Subsequently, it constructs a concept-aware emulator to generate context prompt units, linking extensible knowledge learned from the previously seen DR attributes for zero-shot DR grading. Extensive experiments conducted on eight datasets and various scenarios confirm the superiority of ProME-DR.

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AIIM Journal 2025 Journal Article

Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions

Kai Sun
Siyan Xue
Fuchun Sun
Haoran Sun
Yu Luo
Ling Wang
Siyuan Wang
Na Guo

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.

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AIIM Journal 2025 Journal Article

Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A scoping review

Hamed Jafarpour
Guosong Wu
Cheligeer (Ken) Cheligeer
Jun Yan
Yuan Xu
Danielle A. Southern
Cathy A. Eastwood
Yong Zeng

Background: Narrative electronic medical records (EMR), which include textual notes created by clinicians within healthcare environments, represent a significant resource for documenting various facets of patient care. This form of text exhibits distinctive characteristics, such as the occurrence of grammatically incorrect sentences, abbreviations, frequent acronyms, specialized characters with particular meanings, negation expressions, and sporadic misspellings. As a result, a primary goal in processing these textual notes is to implement effective preprocessing techniques that enhance data quality and ensure consistency across all entries. Recent advancements in algorithms and methodologies within the fields of natural language processing (NLP), machine learning (ML), and large language models (LLM) have prompted researchers to leverage narrative EMR for the detection of hospital adverse events (HAE). Methods: The scoping review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A scoping review protocol was developed and utilized to guide the research process, clearly outlining the eligibility criteria, information sources, search strategies, data management, selection process, data collection procedures, data items, outcomes and prioritization, data synthesis, and meta-bias considerations. The search strategy was implemented across nine engineering and medical electronic databases. Results: The results have indicated that from a total of 3, 264 studies retrieved, 48 unique studies were included in the review. Responses to the research questions were systematically extracted from these studies. The review has identified challenges associated with the preprocessing of narrative texts in EMR for HAE identification. Additionally, three research gaps have been identified: (1) the imperative need for a pipeline to preprocess narrative EMR for the identification of HAE, (2) the necessity for a robust system capable of managing the extensive volume of narrative EMR data, and (3) the requirement for temporal event system, which are essential for effective HAE detection. The study also has underscored the essential role of preprocessing tasks in enhancing the performance of HAE detection. The study has emphasized the importance of extracting N-grams from clinical text, normalizing these N-grams through lemmatization and/or stemming, and establishing semantic feature extraction in preprocessing tasks that significantly affect HAE detection performance. While LLM-based systems naturally incorporate tokenization and normalization processes within their frameworks, it remains crucial to address features that hold semantic relevance to the specific type of HAE during preprocessing. Conclusion: This scoping review has provided valuable insights for researchers focused on HAE detection utilizing narrative EMR data. It has elucidated how preprocessing tasks can elevate the performance of HAE detection and draws attention to neglected research gaps within the field. Addressing these gaps will necessitate further investigation in subsequent research endeavors.

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ICLR Conference 2024 Conference Paper

AlpaGasus: Training a Better Alpaca with Fewer Data

Lichang Chen
Shiyang Li
Jun Yan
Hai Wang
Kalpa Gunaratna
Vikas Yadav
Zheng Tang
Vijay Srinivasan

Large language models~(LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce Alpagasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. Alpagasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and the controlled human study. Its 13B variant matches $>90\%$ performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes \footnote{We apply IFT for the same number of epochs as Alpaca(7B) but on fewer data, using 4$\times$NVIDIA A100 (80GB) GPUs and following the original Alpaca setting and hyperparameters.}. In the experiment, we also demonstrate that our method can work not only for machine-generated datasets but also for human-written datasets. Overall, Alpagasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models.

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EAAI Journal 2024 Journal Article

An improved genetic algorithm with an overlapping strategy for solving a combination of order batching and flexible job shop scheduling problem

Zhifeng Liu
Jiming Zha
Jun Yan
Yueze Zhang
Tianzuo Zhao
Qiang Cheng
Chenyang Cheng

The problem of flexible job shop batch scheduling represents an extension of the flexible manufacturing problem. Different batching strategy significantly affects the effect of subsequent scheduling optimization. Therefore, collaborative optimization of the batching strategy and subsequent scheduling is crucial for improving production efficiency. In this paper, a flexible job shop batch production model considering the set-up time is established by analyzing the coupling relationship between batching strategies and scheduling optimization. Based on the proposed model, an improved genetic algorithm with a three-layer coding mechanism is developed to solve the problem of the solution space expansion caused by batch collaboration. The three-layer coding mechanism includes the batch strategy, operation selection, and machine selection. In addition, an operation overlapping strategy is introduced to improve production efficiency and make production more resilient. The proposed algorithm is tested by experiments on scheduling problems of different sizes. The experimental result shows that the proposed method can significantly improve and efficiently optimize flexible job shop scheduling in batch production.

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NeurIPS Conference 2024 Conference Paper

Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems

Dingbang Liu
Shohei Kato
Wen Gu
Fenghui Ren
Jun Yan
Guoxin Su

Due to the exponential growth of agent interactions and the curse of dimensionality, learning efficient coordination from scratch is inherently challenging in large-scale multi-agent systems. While agents' learning is data-driven, sampling from millions of steps, human learning processes are quite different. Inspired by the concept of Human-on-the-Loop and the daily human hierarchical control, we propose a novel knowledge-guided multi-agent reinforcement learning framework (hhk-MARL), which combines human abstract knowledge with hierarchical reinforcement learning to address the learning difficulties among a large number of agents. In this work, fuzzy logic is applied to represent human suboptimal knowledge, and agents are allowed to freely decide how to leverage the proposed prior knowledge. Additionally, a graph-based group controller is built to enhance agent coordination. The proposed framework is end-to-end and compatible with various existing algorithms. We conduct experiments in challenging domains of the StarCraft Multi-agent Challenge combined with three famous algorithms: IQL, QMIX, and Qatten. The results show that our approach can greatly accelerate the training process and improve the final performance, even based on low-performance human prior knowledge.

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JBHI Journal 2023 Journal Article

An Interpretable Data-Driven Medical Knowledge Discovery Pipeline Based on Artificial Intelligence

Shaobo Wang
Xinhui Du
Guangliang Liu
Hang Xing
Zengtao Jiao
Jun Yan
Youjun Liu
Haichen Lv

Difficulty in knowledge validation is a significant hindrance to knowledge discovery via data mining, especially automatic validation without artificial participation. In the field of medical research, medical knowledge discovery from electronic medical records is a common medical data mining method, but it is difficult to validate the discovered medical knowledge without the participation of medical experts. In this article, we propose a data-driven medical knowledge discovery closed-loop pipeline based on interpretable machine learning and deep learning; the components of the pipeline include Data Generator, Medical Knowledge Mining, Medical Knowledge Evaluation, and Medical Knowledge Application. In addition to completing the discovery of medical knowledge, the pipeline can also automatically validate the knowledge. We apply our pipeline's discovered medical knowledge to a traditional prognostic predictive model of heart failure in a real-world study, demonstrating that the incorporation of medical knowledge can effectively improve the performance of the traditional model. We also construct a scale model based on the discovered medical knowledge and demonstrate that it achieves good performance. To guarantee its medical effectiveness, every process of our pipeline involves the participation of medical experts.

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ICML Conference 2022 Conference Paper

Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Fan Yin
Jieying Jiao
Jun Yan
Guanyu Hu 0002

Basketball shot location data provide valuable summary information regarding players to coaches, sports analysts, fans, statisticians, as well as players themselves. Represented by spatial points, such data are naturally analyzed with spatial point process models. We present a novel nonparametric Bayesian method for learning the underlying intensity surface built upon a combination of Dirichlet process and Markov random field. Our method has the advantage of effectively encouraging local spatial homogeneity when estimating a globally heterogeneous intensity surface. Posterior inferences are performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show that the inferences are accurate and the method is superior compared to a wide range of competing methods. Application to the shot location data of $20$ representative NBA players in the 2017-2018 regular season offers interesting insights about the shooting patterns of these players. A comparison against the competing method shows that the proposed method can effectively incorporate spatial contiguity into the estimation of intensity surfaces.

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YNICL Journal 2021 Journal Article

Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score

Ke Hu
Meng Wang
Yong Liu
Hao Yan
Ming Song
Jun Chen
Yunchun Chen
Huaning Wang

Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.

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AIIM Journal 2020 Journal Article

Real-world data medical knowledge graph: construction and applications

Linfeng Li
Peng Wang
Jun Yan
Yao Wang
Simin Li
Jinpeng Jiang
Zhe Sun
Buzhou Tang

Objective Medical knowledge graph (KG) is attracting attention from both academic and healthcare industry due to its power in intelligent healthcare applications. In this paper, we introduce a systematic approach to build medical KG from electronic medical records (EMRs) with evaluation by both technical experiments and end to end application examples. Materials and Methods The original data set contains 16, 217, 270 de-identified clinical visit data of 3, 767, 198 patients. The KG construction procedure includes 8 steps, which are data preparation, entity recognition, entity normalization, relation extraction, property calculation, graph cleaning, related-entity ranking, and graph embedding respectively. We propose a novel quadruplet structure to represent medical knowledge instead of the classical triplet in KG. A novel related-entity ranking function considering probability, specificity and reliability (PSR) is proposed. Besides, probabilistic translation on hyperplanes (PrTransH) algorithm is used to learn graph embedding for the generated KG. Results A medical KG with 9 entity types including disease, symptom, etc. was established, which contains 22, 508 entities and 579, 094 quadruplets. Compared with term frequency - inverse document frequency (TF/IDF) method, the normalized discounted cumulative gain (NDCG@10) increased from 0. 799 to 0. 906 with the proposed ranking function. The embedding representation for all entities and relations were learned, which are proven to be effective using disease clustering. Conclusion The established systematic procedure can efficiently construct a high-quality medical KG from large-scale EMRs. The proposed ranking function PSR achieves the best performance under all relations, and the disease clustering result validates the efficacy of the learned embedding vector as entity’s semantic representation. Moreover, the obtained KG finds many successful applications due to its statistics-based quadruplet. where N c o m i n is a minimum co-occurrence number and R is the basic reliability value. The reliability value can measure how reliable is the relationship between Si and Oij. The reason for the definition is the higher value of N co(Si, Oij ), the relationship is more reliable. However, the reliability values of the two relationships should not have a big difference if both of their co-occurrence numbers are very big. In our study, we finally set N c o m i n = 10 and R = 1 after some experiments. For instance, if co-occurrence numbers of three relationships are 1, 100 and 10000, their reliability values are 1, 2. 96 and 5 respectively.

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NeurIPS Conference 2019 Conference Paper

Multivariate Distributionally Robust Convex Regression under Absolute Error Loss

Jose Blanchet
Peter Glynn
Jun Yan
Zhengqing Zhou

This paper proposes a novel non-parametric multidimensional convex regression estimator which is designed to be robust to adversarial perturbations in the empirical measure. We minimize over convex functions the maximum (over Wasserstein perturbations of the empirical measure) of the absolute regression errors. The inner maximization is solved in closed form resulting in a regularization penalty involves the norm of the gradient. We show consistency of our estimator and a rate of convergence of order $ \widetilde{O}\left( n^{-1/d}\right) $, matching the bounds of alternative estimators based on square-loss minimization. Contrary to all of the existing results, our convergence rates hold without imposing compactness on the underlying domain and with no a priori bounds on the underlying convex function or its gradient norm.

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TIST Journal 2018 Journal Article

Concept and Attention-Based CNN for Question Retrieval in Multi-View Learning

Pengwei Wang
Lei Ji
Jun Yan
Dejing Dou
Nisansa De Silva
Yong Zhang
Lianwen Jin

Question retrieval, which aims to find similar versions of a given question, is playing a pivotal role in various question answering (QA) systems. This task is quite challenging, mainly in regard to five aspects: synonymy, polysemy, word order, question length, and data sparsity. In this article, we propose a unified framework to simultaneously handle these five problems. We use the word combined with corresponding concept information to handle the synonymy problem and the polysemous problem. Concept embedding and word embedding are learned at the same time from both the context-dependent and context-independent views. To handle the word-order problem, we propose a high-level feature-embedded convolutional semantic model to learn question embedding by inputting concept embedding and word embedding. Due to the fact that the lengths of some questions are long, we propose a value-based convolutional attentional method to enhance the proposed high-level feature-embedded convolutional semantic model in learning the key parts of the question and the answer. The proposed high-level feature-embedded convolutional semantic model nicely represents the hierarchical structures of word information and concept information in sentences with their layer-by-layer convolution and pooling. Finally, to resolve data sparsity, we propose using the multi-view learning method to train the attention-based convolutional semantic model on question–answer pairs. To the best of our knowledge, we are the first to propose simultaneously handling the above five problems in question retrieval using one framework. Experiments on three real question-answering datasets show that the proposed framework significantly outperforms the state-of-the-art solutions.

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IJCAI Conference 2017 Conference Paper

Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification

Jin Wang
Zhongyuan Wang
Dawei Zhang
Jun Yan

Text classification is a fundamental task in NLP applications. Most existing work relied on either explicit or implicit text representation to address this problem. While these techniques work well for sentences, they can not easily be applied to short text because of its shortness and sparsity. In this paper, we propose a framework based on convolutional neural networks that combines explicit and implicit representations of short text for classification. We first conceptualize a short text as a set of relevant concepts using a large taxonomy knowledge base. We then obtain the embedding of short text by coalescing the words and relevant concepts on top of pre-trained word vectors. We further incorporate character level features into our model to capture fine-grained subword information. Experimental results on five commonly used datasets show that our proposed method significantly outperforms state-of-the-art methods.

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AAAI Conference 2017 Conference Paper

Efficiently Answering Technical Questions Ñ A Knowledge Graph Approach

Shuo Yang
Lei Zou
Zhongyuan Wang
Jun Yan
Ji-Rong Wen

More and more users prefer to ask their technical questions online. For machines, understanding a question is nontrivial. Current approaches lack explicit background knowledge. In this paper, we introduce a novel technical question understanding approach to recommending probable solutions to users. First, a knowledge graph is constructed which contains abundant technical information, and an augmented knowledge graph is built on the basis of the knowledge graph, to link the knowledge graph and documents. Then we develop a light weight question driven mechanism to select candidate documents. To improve the online performance, we propose an index-based random walk to support the online search. We use comprehensive experiments to evaluate the effectiveness of our approach on a large scale of real-world query logs. Our system outperforms main-stream search engine and the state-of-art information retrieval methods. Meanwhile, extensive experiments conﬁrm the efﬁciency of our index-based online search mechanism.

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IJCAI Conference 2017 Conference Paper

Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding

Shuangyin Li
Rong Pan
Jun Yan

In the last decade, there has been a huge amount of documents with different types of rich metadata information, which belongs to the Semi-Structured Documents (SSDs), appearing in many real applications. It is an interesting research work to model this type of text data following the way how humans understand text with informative metadata. In the paper, we introduce a Self-paced Compensatory Deep Boltzmann Machine (SCDBM) architecture that learns a deep neural network by using metadata information to learn deep structure layer-wisely for Semi-Structured Documents (SSDs) embedding in a self-paced way. Inspired by the way how humans understand text, the model defines a deep process of document vector extraction beyond the space of words by jointing the metadata where each layer selects different types of metadata. We present efficient learning and inference algorithms for the SCDBM model and empirically demonstrate that using the representation discovered by this model has better performance on semi-structured document classification and retrieval, and tag prediction comparing with state-of-the-art baselines.

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AAAI Conference 2015 Conference Paper

Acronym Disambiguation Using Word Embedding

Chao Li
Lei Ji
Jun Yan

According to the website AcronymFinder. com which is one of the world's largest and most comprehensive dictionaries of acronyms, an average of 37 new human-edited acronym definitions are added every day. There are 379, 918 acronyms with 4, 766, 899 definitions on that site up to now, and each acronym has 12. 5 definitions on average. It is a very important research topic to identify what exactly an acronym means in a given context for document comprehension as well as for document retrieval. In this paper, we propose two word embedding based models for acronym disambiguation. Word embedding is to represent words in a continuous and multidimensional vector space, so that it is easy to calculate the semantic similarity between words by calculating the vector distance. We evaluate the models on MSH Dataset and ScienceWISE Dataset, and both models outperform the state-of-art methods on accuracy. The experimental results show that word embedding helps to improve acronym disambiguation.

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AAAI Conference 2014 Conference Paper

Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network

Yong Luo
Jian Tang
Jun Yan
Chao Xu
Zheng Chen

Word embedding aims to learn a continuous representation for each word. It attracts increasing attention due to its effectiveness in various tasks such as named entity recognition and language modeling. Most existing word embedding results are generally trained on one individual data source such as news pages or Wikipedia articles. However, when we apply them to other tasks such as web search, the performance suffers. To obtain a robust word embedding for different applications, multiple data sources could be leveraged. In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data. This framework takes the word embeddings learned from different data sources as pre-train, and then uses a two-side neural network to unify these embeddings. The pre-trained embeddings are obtained by adapting the recently proposed CBOW algorithm. Since the proposed neural network does not need to re-train word embeddings for a new task, it is highly scalable in real world problem solving. Besides, the network allows weighting different sources differently when applied to different application tasks. Experiments on two real-world applications including web search ranking and word similarity measuring show that our neural network with multiple sources outperforms state-of-the-art word embedding algorithm with each individual source. It also outperforms other competitive baselines using multiple sources.

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AAAI Conference 2011 Conference Paper

Collaborative Users’ Brand Preference Mining across Multiple Domains from Implicit Feedbacks

Jian Tang
Jun Yan
Lei Ji
Ming Zhang
Shaodan Guo
Ning Liu
Xianfang Wang
Zheng Chen

Advanced e-applications require comprehensive knowledge about their users’ preferences in order to provide accurate personalized services. In this paper, we propose to learn users’ preferences to product brands from their implicit feedbacks such as their searching and browsing behaviors in user Web browsing log data. The user brand preference learning problem is challenge since (1) the users’ implicit feedbacks are extremely sparse in various product domains; and (2) we can only observe positive feedbacks from users’ behaviors. In this paper, we propose a latent factor model to collaboratively mine users’ brand preferences across multiple domains simultaneously. By collective learning, the learning processes in all the domains are mutually enhanced and hence the problem of data scarcity in each single domain can be effectively addressed. On the other hand, we learn our model with an adaption of the Bayesian personalized ranking (BPR) optimization criterion which is a general learning framework for collaborative filtering from implicit feedbacks. Experiments with both synthetic and real world datasets show that our proposed model significantly outperforms the baselines.

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