Computer Science ›› 2016, Vol. 43 ›› Issue (9): 107-110.doi: 10.11896/j.issn.1002-137X.2016.09.020

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URTP:A Factorization Model for Recommendation Based on Users,Regions,Time and Products

HU Ya-hui, YANG Sha, LIU Jing, YU Wei, LI Shi-jun, WANG Jun and FANG Qi-qing   

  • Online:2018-12-01 Published:2018-12-01

Abstract: How to recommend a right person with the right products at the right time and place is a challenging topic.First,different users have different culture backgrounds including religion,career,education,preference,etc.Then,different products should be purchased again in different reasonable interval time.And the multi-source heterogeneous,fragmented,various and inconsistent e-commerce data cause problems of sparse data and even cold start.To address these problems,we extracted users’ cultural background with their longitude,latitude and city functional regions.Then,we analyzed the reasonable purchase time and reasonable interval time for different products.And we built a URTP model,which is a factorization model based on users,regions,time and products for recommendation.Experimental results verify the feasibility,effectiveness and efficiency of our algorithm.

Key words: Commodity recommendation,Culture,Big data

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