{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:33:06Z","timestamp":1774539186445,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2021A1515012239"],"award-info":[{"award-number":["2021A1515012239"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurately predicting user\u2013item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze the sparse user\u2013item interaction data without considering their semantic correlations and the structural information hidden in the data. Another limitation is that existing approaches usually embed the users and items into the different embedding spaces in a static way, but ignore the dynamic characteristics of both users and items. In this paper, we propose to learn the dynamic embedding vector trajectories rather than the static embedding vectors for users and items simultaneously. A Metapath-guided Recursive RNN based Shift embedding method named MRRNN-S is proposed to learn the continuously evolving embeddings of users and items for more accurately predicting their future interactions. The proposed MRRNN-S is extended from our previous model RRNN-S which was proposed in the earlier work. Comparedwith RRNN-S, we add the word2vec module and the skip-gram-based meta-path module to better capture the rich auxiliary information from the user\u2013item interaction data. Specifically, we first regard the interaction data of each user with items as sentence data to model their semantic and sequential information and construct the user\u2013item interaction graph. Then we sample the instances of meta-paths to capture the heterogeneity and structural information from the user\u2013item interaction graph. A recursive RNN is proposed to iteratively and mutually learn the dynamic user and item embeddings in the same latent space based on their historical interactions. Next, a shift embedding module is proposed to predict the future user embeddings. To predict which item a user will interact with, we output the item embedding instead of the pairwise interaction probability between users and items, which is much more efficient. Through extensive experiments on three real-world datasets, we demonstrate that MRRNN-S achieves superior performance by extensive comparison with state-of-the-art baseline models.<\/jats:p>","DOI":"10.3390\/a15030080","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T20:09:57Z","timestamp":1646078997000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Dynamic User\u2013Item Interaction with Meta-Path Guided Recursive RNN"],"prefix":"10.3390","volume":"15","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyu","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineerting, University at Buffalo, The State University of New York, Buffalo, NY 14200, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6170-0463","authenticated-orcid":false,"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yin, C., Wang, S., Du, J., and Zhang, M. 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