Exploiting Synergy Between Ontologies and Recommender Systems

TL;DR

This paper explores how combining ontologies with recommender systems can mitigate cold-start issues by leveraging domain knowledge, demonstrated through an empirical evaluation of a research paper recommender system integrated with an automatically extracted ontology.

Contribution

It introduces a novel approach that synergistically combines ontologies and recommender systems to improve cold-start performance and knowledge acquisition.

Findings

The integrated system reduces cold-start problems effectively.

Ontology enhances the recommender system with domain knowledge.

Empirical results show improved recommendation accuracy.

Abstract

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.