Effective rating prediction based on selective contextual information

Abstract Many researchers have realized the importance of contextual information and focused on designing systems that predict user’s contextual preferences. In this respect, several researches have been devoted to Context-Aware Recommender Systems (CARS). One of the remaining issues in these systems especially the collaborative filtering based ones, is determining which contextual information can be adopted to make effective rating prediction. In fact, many contextual dimensions (e.g., location, time, mood etc.) may affect the user’s preferences, but not all of these dimensions are equally important for the rating prediction effectiveness. Many existing CARS approaches cannot fully capture the influence of relevant contextual dimensions and their interaction on the rating, and furthermore cannot obtain a better recommendation performance. To address these issues, we highlight contextual dimensions weighting, study the correlation between them to elicit the most useful ones, and propose two improved rating prediction methods based on collaborative filtering techniques, involving relevant and dependent contextual dimensions. Experimental results, with respect to rating prediction quality and recommendation performance on both public available and large created contextual datasets, show that our proposal outperforms the existing recommender systems especially on the created datasets.