Computer Science ›› 2014, Vol. 41 ›› Issue (3): 71-75.

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Cold-start Recommendation Based on Granular Association Rules

WU Wen-jia and HE Xu   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Recommendation systems have been widely used in many fields such as e-commerce.The cold-start problem is one of difficulties on recommendation systems.This paper designed a cold-start recommendation approach based on granular association rules.First,we used granules to describe users and items.Then we generated rules between users and items through satisfying four measures of granular association rules.Finally,we matched the suitable rules to re-commend items to users.Experiments were undertaken on a publicly available dataset MovieLens.Results show that granular association mining rule can be used for the recommendation on training and testing sets effectively and accurately.

Key words: Granular computing,Association rule,Recommendation system,Cold-start problem,Data mining

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