Abstract
Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.
Highlights
The user similarity and user trust are integrated, and the prediction results are generated by the trust weighting method
The main contributions of this paper are summarized as follows: (1) To solve the problem of dependence of traditional calculation methods on common scoring items, the Bhattacharyya similarity is applied to traditional calculation methods
The similarity calculation is composed of two parts: user’s interest and trust. δ is used as the adjustment factor, the value is [0, 1], and the interval is 0.1
Summary
We briefly review some related studies about collaborative filtering (CF) and trust-based CF recommender systems. E group recommendation algorithm proposed by Ghazarian et al improves the similarity of items on the basis of the original technology, and the specific implementation method is to incorporate support vector machine technology [25] Experiments show that this algorithm strengthens memory-based recommendation and solves the problem of too sparse data. E feature vector of each user especially is dependent on those of his direct neighbors in the social network Experiments show that this method can improve the accuracy of recommendation while alleviating the user’s cold start problem. Authors in [28] propose a new user similarity method that make use of both ratings and trust Experiments show that it can alleviate data sparsity and cold start while improving the recommendation accuracy. Based on the above research works, aiming at the problem of data sparsity in collaborative filtering algorithm, a recommendation approach based on user interest and trust value is proposed
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