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• W3136702521 endingPage "114917" @default.
• W3136702521 startingPage "114917" @default.
• W3136702521 abstract "Recommender systems aim to provide users with recommendations of quality. New evaluation metrics such as diversity, have taken an increasing interest in a wide spectrum of applications, including the ecommerce, due to their ability to improve online revenues. High recommendation diversity allows a higher chance to satisfy the users’ needs. However, in a large market of users and products, the scalability of the system is questionable because of the required computing resources. We present a scalable evolutionary clustering algorithm that allows to target two objectives. The proposed solution balances between the recommendation accuracy and coverage by making an overlapped clustering. In our approach, we use a Genetic Algorithm to assign each user to a main cluster from which he gets his recommendations and to secondary clusters as a candidate neighbor. The performance comparison of our algorithm against classic well-known approaches, such as k-NN based Collaborative Filtering, showed a significant improvement." @default.
• W3136702521 created "2021-03-29" @default.
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• W3136702521 date "2021-09-01" @default.
• W3136702521 modified "2023-09-25" @default.
• W3136702521 title "An overlapping clustering approach for precision, diversity and novelty-aware recommendations" @default.
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• W3136702521 doi "https://doi.org/10.1016/j.eswa.2021.114917" @default.
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