Improving Recommendation Novelty Based on Topic Taxonomy

Clustering has been a widely applied approach to improve the computation efficiency of collaborative filtering based recommendation systems. Many techniques have been suggested to discover the item-to-item, user-to- user, and item-to-user associations within user clusters. However, there are few systems utilize the cluster based topic-to-topic associations to make recommendations. This paper suggests a taxonomy-based recommender system that utilizes cluster based topic-to-topic associations to improve its recommendation quality and novelty

The effects of transparency on perceived and actual competence of a content-based recommender.

Perceptions of a system’s competence influence acceptance of that system [31]. Ideally, users’ perception of competence matches the actual competence of a system. This paper investigates the relation between actual and perceived competence of transparent Semantic Web recommender systems that explain recommendations in terms of shared item concepts. We report an experiment comparing non-transparent and transparent versions of a content-based recommender. Results indicate that in the transparent condition, perceived competence and actual competence (in specific recall) were related, while in the non-transparent condition they were not. Providing insight in what aspects of items triggered their recommendation, by showing the concepts that were the basis for a recommendation, gave users a better assessment of how well the system worked

Towards Time-Aware Semantic enriched Recommender Systems for movies

Abstract. With the World Wide Web moving from passive to active, the role of recommender systems as an aid to make decisions play a very prominent role. This enables its users to find new items of high personal interest, which they were previously unaware of. While traditional approaches have shown the generation of high quality recommendations, the additional use of background knowledge to describe the items and their preferences on a more granular level is still lacking. Furthermore, these approaches do not take into consideration the contextual information, wherein the dimension 'time' plays a significant role. In this paper, we propose a new approach for recommending movies, which semantically enriches the process of generating recommendations by using a taxonomy derived out of different data sources from the LOD-Cloud. Furthermore, the paper also addresses the interplay between the rating behavior of the users and the dimension 'time'