填补法和改进相似度相结合的协同过滤算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-12 Cooperative journals: 《计算机应用研究》

邢长征 金媛

Abstract： Aiming at the sparse user rating data, domestic and foreign scholars have made many improvements on collaborative filtering algorithm, which were summarized as filling user rating data, improving similarity, fusing content to recommend and so on. These single methods can’t solve the problem of data sparseness. In order to solve this problem, this paper proposed a collaborative filtering algorithm which combines the filling data and improving similarity. Firstly, it used the improved filling method which increases the item’s attribute information to fill the user rating data, and then recommended using new similarity method, produced the recommended results, iterated m times. Finally it recommended items according to the average score of scores got in m iterations. The experiment shows that the proposed algorithm has a better recommendation effect than single methods in the case of sparse user rating data.

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