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Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions

Published: 13 September 2022 Publication History

Abstract

Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user’s next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interactions with different behaviors. Though existing sequential methods have achieved advanced performance by considering the varied impacts of interactions with sequential information, a large body of them still have two major shortcomings. Firstly, they usually model different behaviors separately without considering the correlations between them. The transitions from item to item under diverse behaviors indicate some users’ potential behavior manner. Secondly, though the behavior information contains a user’s fine-grained interests, the insufficient consideration of the local context information limits them from well understanding user intentions. Utilizing the adjacent interactions to better understand a user’s behavior could improve the certainty of prediction. To address these two issues, we propose a novel solution utilizing global and personalized graphs for HSR (GPG4HSR) to learn behavior transitions and user intentions. Specifically, our GPG4HSR consists of two graphs, i.e., a global graph to capture the transitions between different behaviors, and a personalized graph to model items with behaviors by further considering the distinct user intentions of the adjacent contextually relevant nodes. Extensive experiments on four public datasets with the state-of-the-art baselines demonstrate the effectiveness and general applicability of our method GPG4HSR.

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MP4 File (RecSys22-10-GPG4HSR-presentation_video.mp4)
Presentation video

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  • (2025)Multi-Behavior Hypergraph Contrastive Learning for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352338337:3(1325-1338)Online publication date: Mar-2025
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            RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
            September 2022
            743 pages
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            Published: 13 September 2022

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            Author Tags

            1. Behavior Transition
            2. Graph Neural Network
            3. Sequential Recommendation
            4. User Intention

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            Cited By

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            • (2025)Multi-Behavior Hypergraph Contrastive Learning for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352338337:3(1325-1338)Online publication date: Mar-2025
            • (2025)User identification network with contrastive clustering for shared-account recommendationInformation Processing & Management10.1016/j.ipm.2024.10405562:3(104055)Online publication date: May-2025
            • (2024)Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688103(465-474)Online publication date: 8-Oct-2024
            • (2024)A Generic Behavior-Aware Data Augmentation Framework for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657682(1578-1588)Online publication date: 10-Jul-2024
            • (2024)Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635857(387-395)Online publication date: 4-Mar-2024
            • (2024)Behavior Interval Heterogeneous Sequential Recommendation2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI62072.2024.10674138(348-354)Online publication date: 21-Jun-2024
            • (2024)SR-DSGA: Session Recommendation for Dual Sequence Based on Graph Neural Network and Multi-AttentionIEEE Access10.1109/ACCESS.2024.344035112(109380-109387)Online publication date: 2024
            • (2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295(111841)Online publication date: Jul-2024
            • (2024)Sequential-hierarchical attention network: Exploring the hierarchical intention feature in POI recommendationWorld Wide Web10.1007/s11280-024-01295-y27:6Online publication date: 24-Sep-2024
            • (2024)FedMLP4SR: Federated MLP-Based Sequential Recommendation SystemArtificial Intelligence and Machine Learning10.1007/978-981-97-1277-9_28(363-375)Online publication date: 3-Apr-2024
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