Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
If you like the beatles you might like...: a tutorial on music recommendation
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Electronic Commerce Research and Applications
Foafing the music: bridging the semantic gap in music recommendation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
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Collaborative filtering, being a popular method for generating recommendations, produces satisfying results for users by providing extremely relevant items. Despite being popular, however, this method is prone to many problems. One of these problems is popularity bias, in which the system becomes skewed towards items that are popular amongst the general user population. These 'obvious' items are, technically, extremely relevant items but fail to be novel. In this paper, we maintain using collaborative filtering methods while still managing to produce novel yet relevant items. This is achieved by utilizing the long-tailed distribution of listening behavior of users, in which their playlists are biased towards a few songs while the rest of the songs, those in the long tail, have relatively low play counts. In addition, we also apply a link analysis method to users and define links between them to create an increasingly fine-grained approach in calculating weights for the recommended items. The proposed recommendation method was available online as a user study in order to measure the relevancy and novelty of the recommended items. Results show that the algorithm manages to include novel recommendations that are still relevant, and shows the potential for a new way of generating novel recommendations.