Collaborative Filtering For Recommendation In Online Social Networks

Autor:	Michael P. O'Mahony, Steven Bourke, Kevin McCarthy, Rachael Rafter, Barry Smyth
Rok vydání:	2012
Předmět:	World Wide Web Computer science Preference data Collaborative filtering Context (language use) Unbundling Recommender system Set (psychology)
Zdroj:	Research and Development in Intelligent Systems XXIX ISBN: 9781447147381 SGAI Conf.
DOI:	10.1007/978-1-4471-4739-8_24
Popis:	In the past recommender systems have relied heavily on the availability of ratings data as the raw material for recommendation. Moreover, popular collaborative filtering approaches generate recommendations by drawing on the interests of users who share similar ratings patterns. This is set to change because of the unbundling of social networks (via open APIs), providing a richer world of recommendation data. For example, we now have access to a richer source of ratings and preference data, across many item types. In addition, we also have access to mature social graphs, which means we can explore different ways of creating recommendations, often based on explicit social links and friendships. In this paper we evaluate a conventional collaborative filtering framework in the context of this richer source of social data and clarify some important new opportunities for improved recommendation performance.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9bef20bdbd1bf6c518144313baff2867 https://doi.org/10.1007/978-1-4471-4739-8_24 Zobrazit plný text záznamu