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- W4387526730 abstract "Calibration is currently becoming a topic in machine learning. In particular, there have been many attempts to realize fair recommendation through the calibration in recommender systems. However, current research on the calibration in recommender systems has been realized by using genres in the film domain. Few studies have been conducted to generalize the calibration method to other recommendation domains. In this paper, we extend the calibration to other recommendation domains using the attributes of knowledge graphs, which are globalized and standardized knowledge source. We conducted extensive experiments using the weighted matrix factorization algorithm and the graph networks of Amazon and Yelp2018 datasets. The results show that calibration based on knowledge graph attributes improves the accuracy and diversity of the recommendation system and is applicable to all recommendation domains. This method is based on the re-ranking method and can be used with all recommended algorithms." @default.
- W4387526730 created "2023-10-12" @default.
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- W4387526730 date "2023-10-11" @default.
- W4387526730 modified "2023-10-12" @default.
- W4387526730 title "Calibration using knowledge graph attributes in recommender systems" @default.
- W4387526730 doi "https://doi.org/10.1117/12.3004143" @default.
- W4387526730 hasPublicationYear "2023" @default.
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