Serendipity-Oriented Clustering Method for Recommender Systems Based on Collaborative Filtering
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Springer International Publishing
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https://link.springer.com/content/pdf/10.1007/978-3-031-17697-5_35
Reference34 articles.
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2. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 12(35), 61–70 (1992)
3. Kotkov, D., Konstan, J.A., Zhao, Q., Veijalainen, J.: Investigating serendipity in recommender systems based on real user feedback. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC 2018), pp. 1341–1350. ACM, Pau, France (2018)
4. Tan, Z., He, L.: An efficient similarity measure for user-based collaborative filtering recommender systems inspired by the physical resonance principle. IEEE Access 5, 27211–27228 (2017)
5. Why do we need to measure similarity? https://people.revoledu.com/kardi/tutorial/Similarity/Applications.html. Accessed 14 Mar 2021
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2. Effects of Binary Similarity Metrics for the Relevance Component of Serendipity-Oriented Clustering Method in Collaborative Filtering;IFAC-PapersOnLine;2024
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