Abstract
Collaborative filtering (CF) is a technique for realizing recommender systems found in e-commerce sites and video streaming sites. Appropriate content recommendations to individual users will improve usability, purchase rates, viewing rates, and corporate profits. Clustering is a technique for automatically classifying and summarizing the data by extracting clusters composed of similar objects. Clustering-based CF extracts clusters of users with similar interests and preferences, and recommends highly preferred contents in the cluster to each user. Rough set C-means (RSCM) is one of the rough clustering methods based on rough set theory that can deal with the uncertainty of belonging of object to clusters considering the granularity of the object space. Probabilistic rough set C-means (PRSCM) is an extension of RSCM based on a probabilistic rough set model. In this study, we propose a collaborative filtering approach based on probabilistic rough set C-means clustering (PRSCM-CF). Furthermore, we verify the recommendation performance of the proposed method through numerical experiments using real-world datasets.
This work was supported by JSPS KAKENHI Grant Number JP20K19886.
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Ubukata, S., Ehara, K. (2023). Collaborative Filtering Based on Probabilistic Rough Set C-Means Clustering. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_20
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