Group Recommendation For Mitigating New User Problem: A Modified OCRG

Authors

  • Harita Mehta Department of Computer Science AND College, University of Delhi
  • Punam Bedi Computer Science Department, University of Delhi
  • Veer Sain Dixit Department of Computer Science ARSD College, University of Delhi

Keywords:

: Group Recommendation, Demographic Filtering, New User Problem, Weighted Item Entropy, Item Popularity, Positive and Negative Ratings

Abstract

Providing Recommendations to a Group of users rather than individuals is an emerging research field. Group Recommenders can predict the interest level of a user by aggregating rating preferences of group members. The New User Problem is inherited by Group Recommender Systems because there is relatively little information about his rating preferences. In this paper, a modified Online Cold
Recommendation Generator (OCRG) is proposed to find group recommendations for new users.

Group of similar users is generated based on positive and negative user preferences. Here, OCRG is extended to identify similar user groups based on demographic attributes of the new user. The proposed modified OCRG aggregates the positive and negative ratings of group members using Item Entropy and Item Popularity, to find attraction, repulsion and balanced inclination of new user towards existing items within the group. The experimental results on Movie Lens dataset show significant improvements in overcoming new user problem in group recommender systems using Balanced Inclination aggregation strategy rather than average aggregation strategy

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Published

2013-04-01

How to Cite

Harita Mehta, Punam Bedi, & Veer Sain Dixit. (2013). Group Recommendation For Mitigating New User Problem: A Modified OCRG. Journal of Network and Innovative Computing, 1, 10. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/20

Issue

Vol. 1 (2013)

Section

Original Article

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