Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
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
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
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In this paper we attempt to retrieve the items in the long-tail for top-N recommendation. That is, to recommend products that the end-user likes, but that are not generally popular, which has been getting more and more notice lately. By analysing the existing issue of current recommendation algorithms, a strategy is proposed that succeeds in maintaining recommendation accuracy while reducing the concentration of the recommendation on popular items in the system. Evaluating on the publicly available Movie lens and Yahoo! datasets, the results show the recommendation algorithm proposed in this work retrieves items in the users' relatively unpopular tastes without losing the performance in their popular tastes, which ultimately results in a better overall accuracy for the system.