Computer Science ›› 2016, Vol. 43 ›› Issue (12): 223-228.doi: 10.11896/j.issn.1002-137X.2016.12.041

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Recommending Commodities Based on User-browsing Tracks

GUO Jun-xia, XU Wen-sheng and LU Gang   

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

Abstract: With the rapid development of E-commerce,recommendation system has been widely used in the Websites.Currently the collaborative filtering recommendation algorithm is the most widely used,however,this kind of methods has sparse matrix and cold-start problems.In order to solve or at least improve these problems,methods based on users’ browsing records were proposed.These methods extract every user’s browsing path sequence called user browsing tracks from the users’ access log,and then recommend preference commodities for the user based on the analyzing result of the tracks.By now,the most methods that recommend commodities for users through analysis browsing path are based on sequence pattern matching or the view of the relationship between commodity and the next browsed commodity.We considered from the view of the relationship between browsing commodities and eventually bought commodities,establishing the user browsing tracks preference model based on this,mining users’ preference,and recommending products for new users.Experiments show that our method plays a certain role in solving the problem of cold-start for new users and enhancing the accuracy and recall rate of the recommendation system in E-commerce.

Key words: Personalized recommendation,Browsing tracks,Cold-start

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