Collaborative user modeling with user-generated tags for social recommender systems

  • Authors:
  • Heung-Nam Kim;Abdulmajeed Alkhaldi;Abdulmotaleb El Saddik;Geun-Sik Jo

  • Affiliations:
  • School of Information Technology and Engineering, University of Ottawa, 800 King Edward, Ottawa, Ontario, Canada K1N 6N5;School of Information Technology and Engineering, University of Ottawa, 800 King Edward, Ottawa, Ontario, Canada K1N 6N5;School of Information Technology and Engineering, University of Ottawa, 800 King Edward, Ottawa, Ontario, Canada K1N 6N5 and College of Computer and Information Sciences, King Saud University, Riy ...;School of Computer and Information Engineering, Inha University, 253 Younghyun-dong, Nam-gu, Incheon 402-751, Republic of Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a user's characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.