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- W4380792007 abstract "Traditional recommender systems create models that can predict user interests based on the user-item relationships. However, these systems often have limited performance due to sparse user behavior data. To address this challenge, researchers are now exploring models for social recommendation that can account for both user- user and user-item relationships based on social networks, and user past behavior, respectively. These models aim to understand each user’s behavior by considering their trusted neighbors and their influence on each other. Specifically, the potential embedding of each user is influenced by their trusted neighbors, who are, in turn, influenced by their own trusted neighbors and social connections. Users’ interests evolve over time as social influence propagates recursively in the social network." @default.
- W4380792007 created "2023-06-16" @default.
- W4380792007 creator A5075833639 @default.
- W4380792007 date "2023-06-15" @default.
- W4380792007 modified "2023-09-24" @default.
- W4380792007 title "RelationalNet using Graph Neural Networks for Social Recommendations" @default.
- W4380792007 doi "https://doi.org/10.31979/etd.u6qc-ubrh" @default.
- W4380792007 hasPublicationYear "2023" @default.
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