Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
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
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Nearest neighbors in high-dimensional data: the emergence and influence of hubs
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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To exploit the enormous potential of niche products, modern information systems must support users in exploring digital libraries and online catalogs. A straight-forward way of doing so is to support browsing the available items, which is in general realized by presenting a user the top-N recommendations for each item. However, recent research indicates that most of the niche products reside in the so-called Long Tail, and simple collaborative filtering-based recommender systems alone do not allow to explore these niche products. In this paper we show that it is not only a popularity problem related to the collaborative filtering approach that makes a portion of the elements of a digital library inaccessible via browsing, but also a consequence of the top N-recommendation approach itself.