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Social Recommender Systems

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Learning Automata Approach for Social Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 820))

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Abstract

Due to the incredible growth of information on the World Wide Web in the recent years, searching and finding contents, products or services that may be of interest for users has become a very difficult task. Recommender systems (RSs) help overcome the information overload problem by studying the preferences of online users and suggesting items they might like. Many companies and Web sites have implemented these systems to recommend products/information/services to their users in a more accurate manner, therefore improving the company’s profits. In this chapter, first we give a brief review on social recommender systems and then we introduce sevela learning automata-based recommended systems.

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Authors and Affiliations

  1. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Alireza Rezvanian

  2. Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami & Mohammad Reza Meybodi

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  1. Alireza Rezvanian
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  2. Behnaz Moradabadi
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  3. Mina Ghavipour
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  4. Mohammad Mehdi Daliri Khomami
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  5. Mohammad Reza Meybodi
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Correspondence to Alireza Rezvanian.

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Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R. (2019). Social Recommender Systems. In: Learning Automata Approach for Social Networks. Studies in Computational Intelligence, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-030-10767-3_8

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