Journal of Northeastern University ›› 2013, Vol. 34 ›› Issue (5): 650-653.DOI: -

• Information & Control • Previous Articles     Next Articles

SRL Recommendation System Model Improving Session Recommendation Diversity

LI Jingjiao1, SUN Limei1,2, WANG Jiao1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. Information and Control Engineering Faculty, Shenyang Jianzhu University, Shenyang 110168, China.
  • Received:2012-11-22 Revised:2012-11-22 Online:2013-05-15 Published:2013-07-09
  • Contact: SUN Limei
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Abstract: Current diversity definitions cannot indicate the recommendation diversity during users’ session and the existing methods for improving recommendation diversity always come at the expense of precision. Session recommendation diversity was proposed. Traditional recommendation systems have poor session recommendation diversity because there are too many repeated nodes in the recommendation trees. SRL model was designed to eliminate the redundancy. By creating session recommendation list for each active user, recommendation loops or weak recommendation loops in the recommendation trees could be avoided based on the proposed model. Experimental results on MovieLens dataset showed that SRL model has substantially higher session recommendation diversity and better recommendation precision.

Key words: session recommendation diversity, recommendation tree, recommendation loop, weak recommendation loop, SRL(session recommendation list)

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