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• W2964131719 abstract "In recent years, how to accurately recommend and solve the opacity of the recommendation system has received more and more attention. Some researchers introduced some auxiliary information into the recommendation system, such as Knowledge Graph (KG). This method improves the accuracy of the recommendation, but there is still the problem of opacity of the recommendation system. In this paper, we propose a Knowledge-aware Path Model Based On User Preferences (KPUP), using knowledge map paths for interpretative recommendations. KPUP forms a preference distribution of users relative to candidates by exploring chain links in the KG. The KPUP can generate path representations by combining the semantics of entities and relationships. By using sequential dependencies in paths, we allow valid reasoning of paths to infer the interaction principle of user items and can be used to predict the final click probability (CTR). In addition, we set up a weighted pool operation to distinguish the contribution points of different paths to the preferences, so that the recommendations are better interpreted. We obtained two data sets, which reflected people’s preferences for movies and books, and conducted a lot of experiments. Compared with the most advanced Baseline, our results are better." @default.
• W2964131719 created "2019-07-30" @default.
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• W2964131719 date "2019-01-01" @default.
• W2964131719 modified "2023-09-23" @default.
• W2964131719 title "An Interpretable Recommendations Approach Based on User Preferences and Knowledge Graph" @default.
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• W2964131719 doi "https://doi.org/10.1007/978-3-030-26354-6_33" @default.
• W2964131719 hasPublicationYear "2019" @default.
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