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A Collaborative Filtering Algorithm for Hierarchical Filtering of Neighbors Based on Users’ Interest

  • Shuwei LeiEmail author
  • Jiang Wu
  • Jin Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

In order to alleviate the sparsity of rating data and single rating similarity, from the perspective of optimizing similarity, this paper proposes a collaborative filtering algorithm for filtering neighbors hierarchically based on user interest (HFNCF). Firstly, this paper adopts the user’s rating number and rating size of each category to calculate user interest, and then improved similarity calculation, and join the interest coincidence degree factor to find similar users with the same interest of the target user, then find similar users with the target user having the largest interest cross-domain, and obtain the neighbor candidate set. Secondly, this paper considers the relative time changes between user ratings and adding time factors to improve the similarity of ratings to select the final similar neighbors. Finally, the comprehensive similarity is obtained by combining the two similarities, so that the target user’s ratings are predicted based on the rating of the final similar neighbors, and generate recommendations by the rating. Experimental proof, the algorithm proposed in this research achieves a lower MAE on MovieLens dataset compared to the traditional algorithm. Therefore, the HFNCF algorithm effectively selects neighbors, and improves the recommendation quality and accuracy.

Keywords

HFNCF User interest User interest coincidence degree Time factor 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Information Science and TechnologyNorthwest UniversityXi’anChina

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