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Like-tasted user groups to predict ratings in recommender systems

Authors
  • Jaffali, Soufiene1
  • Jamoussi, Salma2
  • Smaili, Kamel3
  • Ben Hamadou, Abdelmajid2
  • 1 Imam Abdurrahman bin Faisal University, Dammam, Kingdom of Saudi Arabia , Dammam (Saudi Arabia)
  • 2 University of Sfax, Sfax, 3029, Tunisia , Sfax (Tunisia)
  • 3 SMART, LORIA, Vandoeuvre les Nancy, 54506, France , Vandoeuvre les Nancy (France)
Type
Published Article
Journal
Social Network Analysis and Mining
Publisher
Springer Vienna
Publication Date
Jun 06, 2020
Volume
10
Issue
1
Identifiers
DOI: 10.1007/s13278-020-00643-w
Source
Springer Nature
Keywords
License
Yellow

Abstract

Recommendation systems have gained the intention of many researchers due to the growth of the business of personalizing, sorting and suggesting products to customers. Most of the rating prediction in recommendation systems are based on customer preferences or on the historical behavior of similar customers. The similarity between customers is generally measured by the number of times customers liked or disliked the same item. Given the huge number and the variety of items, many customers cannot be considered as similar, as they did not evaluate the same items, even if they have similar tastes. This paper presents a new method of rating prediction in recommendation systems. The proposed method starts by identifying the taste directions or the interest centers based on the users’ demographic information combined with their previous evaluations. Thus, it uses the principal component analysis to retrieve the major taste orientations. According to these orientations, user groups are created. Then, for each group, it generates a prediction model that will be used to predict unknown rates of users within the corresponding group. In order to assess the accuracy of the proposed method, we compare its results with four baseline methods, namely: RegSVD, BiasedMF, SVD++ and MudRecS. The results prove that the proposed algorithm is more accurate than the baseline algorithms.

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