Personalized Recommendation via Multi-dimensional Meta-paths Temporal Graph Probabilistic Spreading |
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| Institution: | 1. College of Computer and Information, Hohai University, Nanjing 211100, PR China;2. School of Computer and Information, Anqing Normal University, Anqing 246133, PR China;3. School of Foreign Languages, Anqing Normal University, Anqing 246133, PR China;4. School of Software Engineering, Jinling Institute of Technology, Nanjing 211169, PR China;1. Institute of Finance Engineering in School of Management/School of Emergency Management, Jinan University, Guangzhou 510632, China;2. School of Emergency Industry, Guangzhou Pearl-River College of Vocational Technology, Huizhou 516131, China;3. Guangdong Emergency Technology Research Center of Risk Evaluation and Prewarning on Public Network Security, Guangzhou 510632, China;1. School of Economics and Management, Harbin Engineering University, Harbin 150001, China;2. Management School, Harbin University of Commerce, Harbin 150028, China;3. Department of Computer Science and Information Engineering, Asia University, Taichung, 41354, Taiwan;4. Department of Computer Science and Engineering, Kyung Hee University, Republic of Korea |
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| Abstract: | Since meta-paths have the innate ability to capture rich structure and semantic information, meta-path-based recommendations have gained tremendous attention in recent years. However, how to composite these multi-dimensional meta-paths? How to characterize their dynamic characteristics? How to automatically learn their priority and importance to capture users' diverse and personalized preferences at the user-level granularity? These issues are pivotal yet challenging for improving both the performance and the interpretability of recommendations. To address these challenges, we propose a personalized recommendation method via Multi-Dimensional Meta-Paths Temporal Graph Probabilistic Spreading (MD-MP-TGPS). Specifically, we first construct temporal multi-dimensional graphs with full consideration of the interest drift of users, obsolescence and popularity of items, and dynamic update of interaction behavior data. Then we propose a dimension-free temporal graph probabilistic spreading framework via multi-dimensional meta-paths. Moreover, to automatically learn the priority and importance of these multi-dimensional meta-paths at the user-level granularity, we propose two boosting strategies for personalized recommendation. Finally, we conduct comprehensive experiments on two real-world datasets and the experimental results show that the proposed MD-MP-TGPS method outperforms the compared state-of-the-art methods in such performance indicators as precision, recall, F1-score, hamming distance, intra-list diversity and popularity in terms of accuracy, diversity, and novelty. |
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