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New Technology of Library and Information Service  2015, Vol. 31 Issue (5): 34-41    DOI: 10.11925/infotech.1003-3513.2015.05.05
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An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering
Jiang Shuhao1, Pan Xuhua1, Xue Fuliang2
1 Information Engineering College, Tianjin University of Commerce, Tianjin 300134, China;
2 Business School, Tianjin University of Finance and Economics, Tianjin 300222, China
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[Objective] To optimize the diversity of the recommendation list by clustering weight redistribution. [Methods] This paper presents an algorithm to improve the recommendation diversity. Clustering is based on item scores. Clustering weight redistribution algorithm is used to reassign each clustering weight, and final recommendation list is generated from each cluster according to the weight. [Results] Experimental results show that z-diversity values of the recommendation list generated is increased by 0.46, 0.65 and 1.88 respectively for three algorithms on MovieLens data set, and z-diversity values is increased by 0.38, 0.49 and 0.76 respectively on Book-Crossing data set, when threshold is reduced from 20 to 1. [Limitations] This algorithm only applies to improve the recommendation list and does not involve the aggregate diversity. [Conclusions] This algorithm effectively improves diversity, while ensuring accuracy and lower time complexity compared with bounded greedy algorithm.

Key wordsItem clustering      Recommendation diversity      Optimization algorithm      Customer satisfaction      Collaborative filtering     
Received: 31 October 2014      Published: 11 June 2015
:  TP301.6  

Cite this article:

Jiang Shuhao, Pan Xuhua, Xue Fuliang. An Independent Recommendation Diversity Optimization Algorithm Based on Item Clustering. New Technology of Library and Information Service, 2015, 31(5): 34-41.

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