Exploring Latent Preferences for Context-Aware Personalized Recommendation Systems

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 that personalizes 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 resulted recommendation. Experimental results show a potential improvement to the quality of the recommendation in terms of accuracy when compared with state-of-the-art algorithms.