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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Interaction Based Content Recommendation in Online Communities
verfasst von : Surya Nepal, Cécile Paris, Payam Aghaei Pour, Jill Freyne, Sanat Kumar Bista
Erschienen in: User Modeling, Adaptation, and Personalization
Verlag: Springer Berlin Heidelberg
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Content recommender systems have become an invaluable tools in online communities where a huge volume of content items are generated for users to consume, making it difficult for users to find interesting content. Many recommender systems leverage articulated social networks or profile information (e.g, user background, interest, etc.) for content recommendation. These recommenders largely ignore the implied networks defined through user interactions. Yet these play an important role in formulating users’ common interests. We propose an interaction based content recommender which leverages implicit user interactions to determine the relationship trust or strength, generating a richer, more informed implied network. An offline analysis on a 5000 person, 12 week dataset from an online community shows that our approach outperforms algorithms which focus on articulated networks that do not consider relationship trust or strength.