Knowledge-based recommendations of media content: case magazine articles

Sari Vainikainen, Magnus Melin, Caj Södergård

    Research output: Contribution to journalArticleScientificpeer-review


    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.
    Original languageEnglish
    Pages (from-to)169-181
    JournalJournal of Print and Media Technology Research
    Issue number3
    Publication statusPublished - 2013
    MoE publication typeA1 Journal article-refereed


    • recommendation systems
    • personalization
    • semantics
    • semantic web
    • linked data
    • media services
    • metadata
    • user profiles
    • ontology


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