Exploring Latent Preferences for Context-Aware Personalized Recommendation Systems
Journal Article
al., Mohammed F. Alhamid, et . 2016
Publication Online URL:
Issue Number:
99
Volume Number:
1-9
Publication Abstract:
Context-aware recommendations offer the potential of exploiting social contents and utilize related tags and rating information to personalize the search for content considering a given context.Recommendation systems tackle the problem of trying to identify relevant resources from the vast number of choices available online. In this study, we propose a new recommendation model thatpersonalizes recommendations and improves the user experience by analyzing the context when a user wishes to access multimedia content. We conducted empirical analysis on a dataset from last.fm to demonstrate the use of latent preferences for ranking items under a given context. Additionally, we use an optimization function to maximize the mean average precision measure of the resultedrecommendation. Experimental results show a potential improvement to the quality of therecommendation in terms of accuracy when compared with state-of-the-art algorithms.