A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

Maryam Jallouli, Sonia Lajmi and Ikram Amous
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Maryam Jallouli: Miracl Laboratory, Technopole of Sfax, Sfax University, Tunisia
Sonia Lajmi: Miracl Laboratory, Technopole of Sfax, Sfax University, Tunisia & Al Baha University, Al Baha, Saudi Arabia
Ikram Amous: Miracl Laboratory, Technopole of Sfax, Sfax University, Tunisia

International Journal of Strategic Information Technology and Applications (IJSITA), 2018, vol. 9, issue 4, 38-51

Abstract: In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

Date: 2018
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