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Novel Weighted Interest Similarity Measurement for ...
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Abstract: This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps.
(PDF) Novel Weighted Interest Similarity Measurement for ...
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2019年7月27日 — This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps.
Novel Weighted Interest Similarity Measurement for ...
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由 B Hawashin 著作2019被引用 17 次 — Abstract - This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps.
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Novel Weighted Interest Similarity Measurement for ...
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Abstract: This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps.
Novel Weighted Interest Similarity Measurement for Recommender ...
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https://colab.ws › SDS.2019.8768548
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This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps. Although some works were ...
Novel Weighted Interest Similarity Measurement for ...
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Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp · List of references · Publications that cite this publication.
Novel Weighted Interest Similarity Measurement for ...
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Bibliographic details on Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp.
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Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp. Bilal Hawashin, Darah Aqel, Shadi Alzu'bi, Y. Jararweh. 2019 ...
A Novel Bayesian Similarity Measure for Recommender
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由 G Guo 著作被引用 419 次 — Similarity is defined as the inverse normalization of user distance, which is computed by the weighted average of rating distances and of importance weights ...
7 頁
An improved collaborative filtering recommendation ...
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2024年8月26日 — To accurately calculate the similarity between users, the timestamp information is employed to pre-filter the user preference. When recommending ...