{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:47:55Z","timestamp":1777042075419,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the fundamental research funds for the central universities","award":["N180503017"],"award-info":[{"award-number":["N180503017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Session-based recommendations aim to predict a user\u2019s next click based on the user\u2019s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user\u2019s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users\u2019 interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users\u2019 interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users\u2019 long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.<\/jats:p>","DOI":"10.3390\/e23111500","type":"journal-article","created":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T08:10:22Z","timestamp":1636704622000},"page":"1500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Personal Interest Attention Graph Neural Networks for Session-Based Recommendation"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiangde","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Sciences, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2051-4290","authenticated-orcid":false,"given":"Xiaojun","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Sciences, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation","volume":"33","author":"Zhao","year":"2020","journal-title":"IEEE Trans. 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