Abstract
A recommender system can effectively solve the problem of information overload in the era of big data. Recent research on recommender systems, specifically Collaborative Filtering, has focused on Matrix Factorization methods, which have been shown to have excellent performance. However, these methods do not pay attention to the influence of a user’s rating characteristics, which are especially important for the accuracy of prediction or recommendation. Therefore, in order to get better performance, we propose a novel method based on matrix factorization. We consider that the user’s rating score is composed of two parts: the real score, which is decided by the user’s preferences; and the bias score, which is decided by the user’s rating characteristics. We then analyze the user’s historical behavior to find his rating characteristics by using the matrix factorization technique and use them to adjust the final prediction results. Finally, by comparing with the latest algorithms on the open datasets, we verified that the proposed method can significantly improve the accuracy of recommender systems and achieve the best performance in terms of prediction accuracy criterion over other state-of-the-art methods.
Highlights
With the rapid development of the Internet, network information has increased exponentially, leading to rising difficulties in getting useful information
The remainder of this paper is organized as follows: In Section 2, we introduce the Collaborative Filtering (CF) methods and the matrix factorization techniques
The predicted score calculated by Equation (25) may not be an integer, while the user’s rating scores are all integers in the test data; the prediction results are rounded and the integer obtained is used as the final prediction score
Summary
With the rapid development of the Internet, network information has increased (and continues to grow) exponentially, leading to rising difficulties in getting useful information. Recommender systems can accurately suggest valuable information to users because of the Collaborative Filtering (CF) [4,5,6] algorithm, which does not need to provide extra information (such as content of the item), and can make accurate recommendations on the basis of the user’s historical behavior only, such as clicking, browsing, and rating. This is an efficient recommendation algorithm and one of the most widely used. Other studies [7,8] deal with the content and collaborative-based hybrid mechanisms in multimedia information retrieval
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