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Standard collaborative filtering approaches for top-N recommendation arebiased toward popular items. As a result, they recommend items that users arelikely aware of and under-represent long-tail items. This is inadequate, bothfor consumers who prefer novel items and because concentrating on popular itemspoorly covers the item space, whereas high item space coverage increasesproviders' revenue.  We present an approach that relies on historical rating data to learn userlong-tail novelty preferences. We integrate these preferences into a genericre-ranking framework that customizes balance between accuracy and coverage. Weempirically validate that our proposedframework increases the novelty ofrecommendations. Furthermore, by promoting long-tail items to the right groupof users, we significantly increase the system's coverage while scalablymaintaining accuracy. Our framework also enables personalization of existingnon-personalized algorithms, making them competitive with existing personalizedalgorithms in key performance metrics, including accuracy and coverage.

A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage