{"type":"ScholarlyArticle","title":"Variational Graph Auto-Encoder Driven Graph Enhancement for Sequential Recommendation","authors":[{"name":"Yuwen Liu","slug":"yuwen-liu","given":"Yuwen","family":"Liu","affiliation":""},{"name":"Lianyong Qi","slug":"lianyong-qi","given":"Lianyong","family":"Qi","affiliation":""},{"name":"Xingyuan Mao","slug":"xingyuan-mao","given":"Xingyuan","family":"Mao","affiliation":""},{"name":"Weiming Liu","slug":"weiming-liu","given":"Weiming","family":"Liu","affiliation":""},{"name":"Shichao Pei","slug":"shichao-pei","given":"Shichao","family":"Pei","affiliation":""},{"name":"Fan Wang","slug":"fan-wang","given":"Fan","family":"Wang","affiliation":""},{"name":"Xuyun Zhang","slug":"xuyun-zhang","given":"Xuyun","family":"Zhang","affiliation":""},{"name":"Amin Beheshti","slug":"amin-beheshti","given":"Amin","family":"Beheshti","affiliation":""},{"name":"Xiaokang Zhou","slug":"xiaokang-zhou","given":"Xiaokang","family":"Zhou","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2025/350","url":"https://ijcai.org/proceedings/2025/350","pdf":"https://ijcai.org/proceedings/2025/0350.pdf","markdown":"https://ijcai.org/proceedings/2025/350.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/350","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":350,"pages":{"first":3144,"last":3152},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}