Knowledge-based recommendations of media content: case magazine articles

A successful media service must ensure that its content grabs the attention of the audience. Recommendations are a central way to gain attention. The drawback of current collaborative and content-based recommendation systems is their shallow understanding of the user and the content. In this work, we propose recommenders with a deep semantic knowledge of both user and content. We express this knowledge with the tools of semantic web and linked data, making it possible to capture multilingual knowledge and to infer additional user interests and content meanings. In addition, linked data allows knowledge to be automatically derived from various sources with minimal user input. We apply our methods on magazine articles and show, in a user test with 119 participants, that semantic methods generate relevant recommendations. Semantic methods are especially strong when there is little initial information about the user and the content. We also show how user modelling can help avoiding the recommendation of unsuitable items.