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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.17598 |
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| _version_ | 1866917652674379776 |
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| author | Jung, Hoin Cho, Hyunsoo Choi, Myungje Lee, Joowon Park, Jung Ho Kang, Myungjoo |
| author_facet | Jung, Hoin Cho, Hyunsoo Choi, Myungje Lee, Joowon Park, Jung Ho Kang, Myungjoo |
| contents | When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and unified information about users. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement of the performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering Jung, Hoin Cho, Hyunsoo Choi, Myungje Lee, Joowon Park, Jung Ho Kang, Myungjoo Information Retrieval Artificial Intelligence Machine Learning Social and Information Networks When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and unified information about users. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement of the performance. |
| title | Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering |
| topic | Information Retrieval Artificial Intelligence Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2404.17598 |