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- W4387337508 abstract "In recent years, additional information in the side of both users and items are more and more common in the natural world. Traditional recommendation methods mainly utilize the the user-item rating information. These information, if properly used, can benefit to infer the preference of user. But due to the complexity of HIN(heterogeneous information network), HIN based recommendation system is faced with the following challenge: how to properly utilize the attribute of both users and items effectively.To solve such a problem, we propose the approach:coupling collaborative interaction information and semantic information for Top-N Recommendation System(CSRec). For collaborative information,we use graph neural networks to aggregate the neighborhood information. And for semantic information, we use intra-metapath to generate and inter-metapath semantic aggregation between user and item to generate semantic context information. Then we deeply combine two kinds of information and generate recommendation item. Finally, comprensive experiments on three real-world recommendation dataset prove the superiority of our model,in comparison with other baseline models." @default.
- W4387337508 created "2023-10-05" @default.
- W4387337508 creator A5067937212 @default.
- W4387337508 date "2023-08-25" @default.
- W4387337508 modified "2023-10-06" @default.
- W4387337508 title "Collaborative information and semantic information Fusion over Heterogeneous information Network for Top-N Recommendation System" @default.
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- W4387337508 doi "https://doi.org/10.1145/3622896.3622904" @default.
- W4387337508 hasPublicationYear "2023" @default.
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