Augmenting a Feature Set of Movies Using Linked Open Data

Augmenting a feature set using mappings to the Web of data is an up-and-coming way to enrich data in the original dataset. Those enrichments are valuable especially for the recent preference learning algorithms and recommender systems. In this paper, we describe the process of mapping and augmenting the movie ratings dataset MovieTweetings from the perspective of RecSysRules 2015 Challenge. The ad-hoc queries to DBpedia are used as an underlying concept. To the best of our knowledge, there is no existing mapping dataset of movies for MovieTweetings. We also provide a brief discussion about the benefits of the augmented feature set for an elementary rule-based representation of the user preferences.

• Type: Journal
• Year: 2015
• Where: RULEML
• Authors: Jaroslav Kuchar
• Keywords: Internet Technology, RULEML 2015