Integrating ratings and pairwise preferences in recommender systems
User preferences in the form of absolute evaluations such as user ratings or clicks are widely used in many Recommender Systems (RSs). However, such type of preferences have some disadvantages. For instance, users can not further refine the preferences for items that are scored with the same rating (e.g., are both 5 stars or both liked). In our research work, as an alternative way of modeling user preferences and compute recommendations, we have been focusing on pairwise preferences, such as, item i is preferred to item j. We aim at building RSs by combining both ratings and pairwise preferences in order to make the best use of this mixed preference data. Our results demonstrate that it is possible to effectively use pairwise preferences to generate accurate recommendations and that there are specific conditions/situations where pairwise preferences elicitation is more meaningful and useful.