{"type":"ScholarlyArticle","title":"Spatial-Temporal Perceiving: Deciphering User Hierarchical Intent in Session-Based Recommendation","authors":[{"name":"Xiao Wang","slug":"xiao-wang","given":"Xiao","family":"Wang","affiliation":"University of Electronic Science and Technology of China"},{"name":"Tingting Dai","slug":"tingting-dai","given":"Tingting","family":"Dai","affiliation":"University of Electronic Science and Technology of China"},{"name":"Qiao Liu","slug":"qiao-liu","given":"Qiao","family":"Liu","affiliation":"University of Electronic Science and Technology of China"},{"name":"Shuang Liang","slug":"shuang-liang","given":"Shuang","family":"Liang","affiliation":"University of Electronic Science and Technology of China"}],"abstract":"Session-based recommendation (SBR) aims to predict the next-interacted item based on anonymous users' behavior sequences. The main challenge is how to recognize the user intent with limited interactions to achieve a more accurate inference of user behavior. Existing works usually regard several consecutive items in the current session as intent. However, we argue such intent generation based on temporal transition ignores the fact that each item also has its semantically connected items in the feature space, which can be regarded as spatial intent. The limited consideration of intent fails to capture complex behavioral patterns in real-world scenarios, leading to sub-optimal solutions. To address this issue, we propose the Hierarchical Intent Perceiving Contrastive Learning Framework (HearInt) for SBR, which proposes a hierarchical consideration of intents from both temporal and spatial perspective. Specifically, we first propose that the user's temporal intents are mutually exclusive while the spatial intents are mutually compatible. Following these analyses, we design a Temporal Intent Decoupling module to mitigate the mutual influence of long-term and short-term intents, and a Cross-scale Contrastive Learning task to enhance the consistency of intents across different spatial scales. Experimental results on three real-world datasets exhibit that HearInt achieves state-of-the-art performance.","doi":"10.24963/ijcai.2024/267","url":"https://ijcai.org/proceedings/2024/267","pdf":"https://ijcai.org/proceedings/2024/0267.pdf","markdown":"https://ijcai.org/proceedings/2024/267.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/267","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":267,"pages":{"first":2415,"last":2423},"keywords":["Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"Datasets","title":"Datasets","authors":null,"year":null,"venue":"Datasets","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Diginetica Metrics P@10 MRR@10 P@20 MRR@20 P@10 MRR@10 P@20 MRR@20 P@10 MRR@10 P@20 MRR@20 NARM 42.07 24.88 50.22 24.59 19.17 10.42 23.30 10.70 35.44 15.13 49.70 16.17 STAMP 42.95 24.61 50.96 25.17 22.63 13.12 26.47 13.36 33.98 14.26 45.64 14.32 SR-GNN 43.21 26.07 50.32 26.57 23.41 13.45 27.57 13.72 36.86 15.52 50.73 17.59 GCE-GNN 46.05 27.48 53.63 28.01 29.19 15.55 33.42 15.42 41.54 18.29 54.22 19.04 S 2 -DHCN 46.15 26.85 53.66 27.30 26.22 14.60 31.42 15.05 39.87 17.53 53.18 18.44 COTREC 48.61 29.46 56.17 29.97 30.62 17.65 36.35 18.04 41.88 18.16 54.18 19.07 HIDE 43.95 25.70 51.25 26.20 31.10 16.77 37.12 17.19 40.39 17.45 53.72 18.37 Atten-Mixer 48.63 28.05 56.01 28.57 31.62 18.23 37.24 18.62 40.24 17.32 53.86 18.27 HearInt 49.62 30.02 57.39 30.54 33.87 18.85 39.92 19.27 41.47 18.60 55.02 19.52 Table 2: Performances of comparison approaches on three datasets. 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