Abstract
The recommender systems play an important role in our lives, since it can quickly help users find what they are interested in. Collaborative filtering has become one of the most widely used algorithms in recommender systems due to its simplicity and efficiency. However, when the user's rating data is sparse, the accuracy of the collaborative filtering algorithm for predictive rating is badly reduced. In addition, the similarity calculation method is another important factor that affects the accuracy of the collaborative filtering algorithm recommendation. Faced with these problems, we propose a new collaborative filtering algorithm which based on Gaussian mixture model and improved Jaccard similarity. The proposed model uses Gaussian mixture model to cluster users and items respectively and extracts new features to build a new interaction matrix, which effectively solves the impact of rating data sparsity on collaborative filtering algorithms. Meanwhile, a new similarity calculation method is proposed, which is combined by triangle similarity and Jaccard similarity. Compare our proposed model with four models based on collaborative filtering algorithms on three public datasets. The experimental results show that the proposed model not only mitigates the sparseness of the data, but also improves the accuracy of the rating prediction.
Highlights
The rapid development of the Internet, in recent years, has brought tremendous changes in people’s lives
In order to deal with the above two factors affecting the accuracy of collaborative filtering algorithm, we propose a collaborative filtering algorithm based on Gaussian mixture model and improved Jaccard similarity
Combine the proposed similarity calculation method with GMM clustering to improve the accuracy of rating prediction
Summary
The rapid development of the Internet, in recent years, has brought tremendous changes in people’s lives. Moradi et al proposed a collaborative filtering algorithm that uses novel graph clustering to recommend invisible items to users [28]. Sun et al proposed triangle similarity is combined with Jaccard similarity to measure the similarity between users or items [36] These improved similarity calculation methods are not combined with clustering algorithms to optimize CF. The Gaussian mixture model is used to cluster the user item rating matrix, and feature extraction is performed to construct a new user item interaction matrix. Combining Jaccard similarity and triangle similarity, a new similarity calculation method is proposed to improve the accuracy of the rating. Combine the proposed similarity calculation method with GMM clustering to improve the accuracy of rating prediction. A summary of the contributions to this paper is given
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