Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

TL;DR

This paper reviews how knowledge, especially semantic information from user context, influences the effectiveness of recommender systems and proposes new ways to enhance knowledge-based user profiles.

Contribution

It provides a comprehensive review of recommender systems focusing on the role of knowledge in user profiling and filtering, highlighting the impact of semantic information.

Findings

Semantic information significantly improves knowledge-based recommender performance.

Knowledge extracted from user context has a substantial impact on recommendations.

Proposed new methods for enhancing knowledge-based user profiles.

Abstract

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.