{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:59Z","timestamp":1758672899823,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Recommender systems play a critical role in many applications by providing personalized recommendations based on user interactions. However, it remains a major challenge to capture complex sequential patterns and address noise in user interaction data. While advanced neural networks have enhanced sequential recommendation by modeling high-order item dependencies, they typically assume that the noisy interaction data as the user's preferred preferences. This assumption can lead to suboptimal recommendation results. We propose a Variational Graph Auto-Encoder driven Graph Enhancement (VGAE-GE) method for robust augmentation in sequential recommendation. Specifically, our method first constructs an item transition graph to capture higher-order interactions and employs a Variational Graph Auto-Encoder (VGAE) to generate latent variable distributions. By utilizing these latent variable distributions for graph reconstruction, we can improve the item representation. Next, we use a Graph Convolutional Network (GCN) to transform these latent variables into embeddings and infer more robust user representations from the updated item embeddings. Finally, we obtain the reconstructed user check-in data, and then use a Mamba-based recommender to make the recommendation process more efficient and the recommendation results more accurate. Extensive experiments on five public datasets demonstrate that our VGAE-GE model improves recommendation performance and robustness.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/350","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"3144-3152","source":"Crossref","is-referenced-by-count":0,"title":["Variational Graph Auto-Encoder Driven Graph Enhancement for Sequential Recommendation"],"prefix":"10.24963","author":[{"given":"Yuwen","family":"Liu","sequence":"first","affiliation":[{"name":"China University of Petroleum (East China)"},{"name":"Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China)"},{"name":"Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software"},{"name":"Nanjing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyuan","family":"Mao","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China)"},{"name":"Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiming","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichao","family":"Pei","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macquarie University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[{"name":"Macquarie University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Kansai University"},{"name":"RIKEN Center for Advanced Intelligence Project"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:47Z","timestamp":1758627227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/350"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/350","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}