A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage
This addresses the problem for consumers and providers by improving recommendation novelty and item space coverage, though it is incremental as it builds on existing re-ranking methods.
The paper tackles the bias of standard collaborative filtering towards popular items in top-N recommendation by introducing a framework that learns user long-tail novelty preferences and re-ranks items to balance accuracy and coverage, resulting in increased novelty and significant coverage gains while maintaining accuracy.
Standard collaborative filtering approaches for top-N recommendation are biased toward popular items. As a result, they recommend items that users are likely aware of and under-represent long-tail items. This is inadequate, both for consumers who prefer novel items and because concentrating on popular items poorly covers the item space, whereas high item space coverage increases providers' revenue. We present an approach that relies on historical rating data to learn user long-tail novelty preferences. We integrate these preferences into a generic re-ranking framework that customizes balance between accuracy and coverage. We empirically validate that our proposedframework increases the novelty of recommendations. Furthermore, by promoting long-tail items to the right group of users, we significantly increase the system's coverage while scalably maintaining accuracy. Our framework also enables personalization of existing non-personalized algorithms, making them competitive with existing personalized algorithms in key performance metrics, including accuracy and coverage.