{"type":"ScholarlyArticle","title":"KDDC: Knowledge-Driven Disentangled Causal Metric Learning for Pre-Travel Out-of-Town Recommendation","authors":[{"name":"Yinghui Liu","slug":"yinghui-liu","given":"Yinghui","family":"Liu","affiliation":"Zhejiang University of Technology"},{"name":"Guojiang Shen","slug":"guojiang-shen","given":"Guojiang","family":"Shen","affiliation":"Zhejiang University of Technology"},{"name":"Chengyong Cui","slug":"chengyong-cui","given":"Chengyong","family":"Cui","affiliation":"Zhejiang University of Technology"},{"name":"Zhenzhen Zhao","slug":"zhenzhen-zhao","given":"Zhenzhen","family":"Zhao","affiliation":"Zhejiang University of Technology"},{"name":"Xiao Han","slug":"xiao-han","given":"Xiao","family":"Han","affiliation":"City University of Hong Kong"},{"name":"Jiaxin Du","slug":"jiaxin-du","given":"Jiaxin","family":"Du","affiliation":"Zhejiang University of Technology"},{"name":"Xiangyu Zhao","slug":"xiangyu-zhao","given":"Xiangyu","family":"Zhao","affiliation":"City University of Hong Kong"},{"name":"Xiangjie Kong","slug":"xiangjie-kong","given":"Xiangjie","family":"Kong","affiliation":"Zhejiang University of Technology"}],"abstract":"Pre-travel recommendation is developed to provide a variety of out-of-town Point-of-Interests (POIs) for users planning to travel away from their hometowns but have not yet decided on their destination. Existing out-of-town recommender systems work on constructing users' latent preferences and inferring travel intentions from their check-in sequences. However, there are still two challenges that hamper the performance of these approaches: i) Users' interactive data (including hometown and out-of-town check-ins) tend to be rare, and while candidate POIs that come from different regions contain various semantic information; ii) The causes for user check-in include not only interest but also conformity, which are easily entangled and overlooked. To fill these gaps, we propose a Knowledge-Driven Disentangled Causal metric learning framework (KDDC) that mitigates interaction data sparsity by enhancing POI semantic representation and considers the distributions of two causes (i.e., conformity and interest) for pre-travel recommendation. Specifically, we pretrain a constructed POI attribute knowledge graph through a segmented interaction method and POI semantic information is aggregated via relational heterogeneity. In addition, we devise a disentangled causal metric learning to model and infer userrelated representations. Extensive experiments on two real-world nationwide datasets display the consistent superiority of our KDDC over state-of-theart baselines.","doi":"10.24963/ijcai.2024/244","url":"https://ijcai.org/proceedings/2024/244","pdf":"https://ijcai.org/proceedings/2024/0244.pdf","markdown":"https://ijcai.org/proceedings/2024/244.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/244","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":244,"pages":{"first":2207,"last":2215},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Ding et al., 2019] Jingtao Ding, Guanghui Yu, Yong Li, De- peng Jin, and Hui Gao. 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