Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering

Abstract

Context-aware Recommender Systems aim to provide users with better recommendations for their current situation. Although evaluations of recommender systems often focus on accuracy, it is not the only important aspect. Often recommendations are overspecialized, i.e. all of the same kind. To deal with this problem, other properties can be considered, such as serendipity. In this paper, we study how an ontology-based and context-aware pre-filtering technique which can be combined with existing recommendation algorithm performs in ranking tasks. We also investigate the impact of our method on the serendipity of the recommendations. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can improve the accuracy and serendipity.

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

“…Some recommender systems suffer from their over-specialization (sometimes referred to as a serendipity problem). It is observed when the RS produces recommendations with minimal novelty, i.e., all of the same kind [22]. Recently, there is also an increasing interest in privacy awareness when handling user data and explainability of recommendations [23], [24].…”

Improving Re-rankCCP with Rules Quality Measures

“…They showed that usage of such micro profiles gave a significant improvement in the prediction accuracy in the movie domain while considering time as a context variable. Pre-filtering approach which utilizes ontological user profiles was proposed by Karpus et al [6] Contextual post-filtering applies context after traditional recommendation process. It means that from a predicted set of recommendations we select just those that match current user context.…”