Improving Data Sparsity and Cold Start in Recommender Systems using Social Transferable Data

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-42 Number-1
Year of Publication : 2016
Authors : U.Kanakayya, Mr.D. Srikar, Mr.S.V. Surya Narayana
  10.14445/22312803/IJCTT-V42P109

MLA

U.Kanakayya, Mr.D. Srikar, Mr.S.V. Surya Narayana  "Improving Data Sparsity and Cold Start in Recommender Systems using Social Transferable Data". International Journal of Computer Trends and Technology (IJCTT) V42(1):51-58, December 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Present systems will suffer from the development that users normally rate solely a restricted variety of things and Cold begin refers to the issue in bootstrapping the RSs for brand new users or new items. The principle of cooperative Filtering is to combination the ratings of like users. However, the according matrix of user-item ratings is sometimes terribly thin thanks to users’ lack of information or incentives to rate things. additionally, for the new users or new things, in general, they report or receive solely a number of or no ratings. each problems can forestall the CF from providing effective recommendations, as a result of users’ preference is tough to extract. during this paper, rather than finding similarity from rating data, we tend to propose a brand new approach that predicts the ratings of things by considering directed and transitive trust with timestamps and profile similarity from the social network at the side of the user-rated data. In cases wherever the trust and therefore the rating details of users from the system is absent, we tend to still create use of the social information of the users just like the merchandise likable by the user, user’s social profileeducation standing, location etc. to form recommendation. Experimental analysis proves that our approach will improve the user recommendations at the acute levels of scantness in user-rating information. we tend to additionally show that our approach works significantly well for cold-start users beneath the circumstances wherever cooperative filtering approach fails.

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Keywords
Recommender system, cooperative filtering, Sparsity, Cold-start drawback, Social behavior.