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Effective page recommendation algorithms based on distributed learning automata

Forsati, R ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ICCGI.2009.14
  3. Abstract:
  4. Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is less complex than other algorithms with respect to memory usage and computational cost too
  5. Keywords:
  6. Learning automata ; Machine learning ; Personalization ; Web mining ; Computational costs ; Data sets ; Distributed learning ; Information overloads ; Information spaces ; Learned patterns ; Learning automata ; Machine-learning ; Memory usage ; Other algorithms ; Personalizations ; Recommendation algorithms ; Recommender systems ; Web Mining ; Web page ; Automata theory ; Computer science ; Education ; Information technology ; Robot learning ; Robots ; Translation (languages) ; Learning algorithms
  7. Source: 4th International Multi-Conference on Computing in the Global Information Technology, ICCGI 2009, 23 August 2009 through 29 August 2009, Cannes, La Bocca ; 2009 , Pages 41-46 ; 9780769537511 (ISBN)
  8. URL: http://ieeexplore.ieee.org/document/5279774/?reload=true