Collaborative filtering algorithm combining trust relationship and item preference

Xiaoyan Zhang; Qi Guo

doi:10.1117/12.2626445

21 December 2021 Collaborative filtering algorithm combining trust relationship and item preference

Xiaoyan Zhang, Qi Guo

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560E (2021) https://doi.org/10.1117/12.2626445
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

To solve of the traditional collaborative filtering recommendation algorithm has some problems, such as sparse data and difficult cold start, we proposed a collaborative filtering (TCF) recommendation algorithm based on trust relation and item preference. The algorithm performs triple processing on user ratings. First it introduces a correction factor to optimize the traditional similarity calculation. Then it uses user similarity to mine potential trust relationships between users. And taking into account the complex real-world relationship between users, using distrust information to filter users and get new ratings. Finally, a new scoring matrix is constructed on the basis of this score, and the improved Tanimoto coefficient is used to calculate the similarity, and the recommendation results are obtained by integrating user trust and item preferences. Experimental results show that the algorithm can effectively improve the quality of recommendations.

Citation Download Citation

Xiaoyan Zhang and Qi Guo "Collaborative filtering algorithm combining trust relationship and item preference", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560E (21 December 2021); https://doi.org/10.1117/12.2626445

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