Fuzzy-genetic approach to context-aware recommender systems based on the hybridization of collaborative filtering and reclusive method techniques

• Published in: Ai communications Vol. 32; no. 2; pp. 125 - 141
• Main Authors: Linda, Sonal, Minz, Sonajharia, Bharadwaj, K.K.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2019
• Subjects: collaborative filtering; Context-aware recommender systems; fuzzy-genetic approach; contextual modeling; reclusive method
• ISSN: 0921-7126; 1875-8452
• DOI: 10.3233/AIC-180593

Recent advancements in web personalization techniques facilitate enhanced web-based services that allow recommender systems (RSs) to incorporate contextual knowledge about users and items as an additional dimension into recommendation process. Context-awareness is one of the important aspects of ubiquitous computing to support cognitive environment and provide services in various e-commerce recommendation applications. Tracking each user’s preferences over various contextual dimensions from their past transactions and providing personalized recommendations to them are the essence of context-aware recommender systems (CARSs). Conventional paradigms for incorporating context in recommendation process cannot fully cover the challenges on several levels of a context-aware system. Our proposed scheme is based on the hybridization of two complementary techniques, collaborative filtering (CF) and reclusive method (RM) to make context valuable at each level of users’ preferences and improve predictive capability of CARSs. Further, a fuzzy real-coded genetic algorithm (Fuzzy-RCGA) approach is incorporated for identifying the influential contextual situations and handling the uncertainty of users’ preferences under various contextual situations. Furthermore, users’ demographic features are utilized for alleviating the problem of data sparsity. The empirical results on two real-world benchmark datasets clearly demonstrate the effectiveness of our proposed schemes for CARS framework.