Abstract
Collaborative filtering recommendation system based on user similarity has been wildly studied because of its broad application. In reality, users keep partial similarity with larger possibility. Computing the whole similarity between users without considering item category is inaccurate when predicting rating for a special category of items by using collaborative filtering recommendation system. Aiming at this problem, a new similarity measurement was given. Based on the new similarity measurement, a new collaborative filtering algorithm named UICF was presented for recommendation. When predicting rating for the special item, UICF chooses the users as nearest neighbors which have the similar rating feature for the items with the same type of the special item, instead of for all the items. Experimental results show the higher quality of the algorithm.
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Zhang, D., Xu, C. (2012). A Collaborative Filtering Recommendation System by Unifying User Similarity and Item Similarity. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 7142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28635-3_17
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DOI: https://doi.org/10.1007/978-3-642-28635-3_17
Publisher Name: Springer, Berlin, Heidelberg
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