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- Title
Collaborative Filtering Algorithm Combining Similarity Measure and Pre-filtering Mode.
- Authors
ZHAO Wentao; TIAN Huanhuan+; FENG Tingting; CUI Ziheng
- Abstract
In the neighborhood-based collaborative filtering algorithms, the calculation of user (item) similarity has an important effect on the result of prediction and recommendation. Most of traditional similarity measures only consider co- rated items, which can get the similarity results quickly. However, the sparse datasets will lower the recommendation accuracy. At present, most of the advanced collaborative filtering algorithms improve the recommendation accuracy by designing complex similarity measures, but often ignore the computation cost in the model. A collaborative filtering algorithm combining similarity measure and pre-filtering mode is proposed in order to generate better recommendation in lower computation time. Firstly, the optimized similarity is obtained by defining the relative rating difference and enumerating the qualitative conditions that should be satisfied. At the same time, the rating preference based on the improved information entropy and the quantity information of user global ratings are considered as the two weight factors, which better distinguishes the differences between users and alleviates the problem of inaccurate similarity calculation under sparse data. Secondly, due to the inherent characteristics of the similarity measure and rating prediction formula, a pre-filtering model is proposed to filter out a large number of unnecessary users and the corresponding ratings, so as to further improve the computational efficiency. Finally, the collaborative filtering algorithm combining similarity measure and pre-filtering mode is obtained. Experimental results on the benchmark datasets indicate that the collaborative filtering algorithm has better recommendation quality and higher time efficiency than other eight comparison algorithms.
- Subjects
RECOMMENDER systems; ENTROPY (Information theory); FILTERS &; filtration; ALGORITHMS
- Publication
Journal of Frontiers of Computer Science & Technology, 2023, Vol 17, Issue 1, p217
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2104065