An Improved Algorithm for Recommendation Diversity Based on the Dissimilarity of Attribute Value
ZHANG Jun
DING Yanhui
JIN Lianxu
Abstract：Recommendation diversity is increasingly becoming an important indicator to evaluate the performance of the recommendation system.There is little consideration of the dissimilarity of the item attribute value for the existing methods of improving the recommendation diversity.In this paper, an improved algorithm for recommendation diversity based on the dissimilarity of attribute value is proposed.Firstly, the dissimilarity of attribute value and item is measured.Secondly, items are clustered according to the item dissimilarity.Finally, combined with clustering information, the initial Top-N recommendation list generated by the existing recommendation algorithm is optimized.Experimental results show that the proposed algorithm can effectively improve the recommendation diversity while maintaining an acceptable level of recommendation accuracy.
Keywords：recommendation systemdissimilaritydiversityTop-N recommendationclustering
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 206-209,298 )