Towards Time-Aware Semantic Enriched Recommender Systems for Movies

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’.

• Type: Journal
• Year: 2015
• Where: RULEML
• Authors: Marko Harasic, Pierre Ahrendt, Alexandru Todor, Adrian Paschke
• Keywords: Internet Technology, RULEML 2015