An Improved Fuzzy Logic Based Recommender System by Integrating Social Tags and Social Networks Information
Asgarali Bouyer
Progress in Computing Applications, Vol. 7, No. 2, 2018.
DOI: https://doi.org/10.6025/pca/2018/7/2/47-55
Link: http://www.dline.info/pca/fulltext/v7n2/pcav7n2_1.pdf
Abstract:
With the rapid development of computers, internet, social media and networks, and other digital multimedia
technologies, it is needed to use a mechanism that can predict the needs and desires of users and recommend the bests for
them. Introducing the social networksâ€™ information into the traditional collaborative filtering (CF) algorithm, the essay
studies the changes of user preference in social networks. Recently a lot of research efforts have been spent on building
recommender systems by utilizing the abundant online social networkâ€™s data. This paper proposes an improved collaborative
filtering algorithm based on fuzzy logic and Social Network Information. The proposed method enhances the accuracy of
recommendations by combining the social tags, fuzzy logic and social networksâ€™ information such as friendship and groupsâ€™
membership. Through the experiment, the improved algorithm has higher accuracy than the traditional filtering algorithms
in the top-N recommendation list. It proves that the social networksâ€™ information of users can affect the userâ€™s preference.