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A Collaborative Filtering Recommendation Based on User Trust and Common Liking Rate

Published: 25 February 2018 Publication History

Abstract

In order to reduce the negative impacts of sparse data, a collaborative filtering recommendation method based on sparse subspace clustering and common liking rate is proposed. Firstly, the sparse subspace clustering method is used to cluster the users, and initial filling of the user's rate data, so that more useful information can be retained. Then, select the user's common scoring data, two users with little difference in rates are selected as the common liking rate set. The similarity of users is calculated according to the common liking rate set. The common liking rate set can better reflect the user similarity and reduce the error. At last, search the nearest neighbor users and generate recommendation result set. The experimental results on real data sets show that the algorithm can predict the user's rate more effectively in the case of sparse data.

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Cited By

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  • (2020)Collaborative filtering recommendation algorithm based on bisecting K-means clusteringInternational Symposium on Artificial Intelligence and Robotics 202010.1117/12.2580026(47)Online publication date: 12-Oct-2020
  • (2019)SCCF Parameter and Similarity Measure Optimization and EvaluationKnowledge Science, Engineering and Management10.1007/978-3-030-29551-6_11(118-127)Online publication date: 28-Aug-2019

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  1. A Collaborative Filtering Recommendation Based on User Trust and Common Liking Rate

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      cover image ACM Other conferences
      ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing
      February 2018
      198 pages
      ISBN:9781450364027
      DOI:10.1145/3193025
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      Publication History

      Published: 25 February 2018

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      Author Tags

      1. Sparse Data
      2. collaborative filtering
      3. common liking rate
      4. sparse subspace

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      View all
      • (2020)Collaborative filtering recommendation algorithm based on bisecting K-means clusteringInternational Symposium on Artificial Intelligence and Robotics 202010.1117/12.2580026(47)Online publication date: 12-Oct-2020
      • (2019)SCCF Parameter and Similarity Measure Optimization and EvaluationKnowledge Science, Engineering and Management10.1007/978-3-030-29551-6_11(118-127)Online publication date: 28-Aug-2019

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