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

arXiv: https://arxiv.org/abs/1803.00146 · HTML: https://mlsanity.com/paper/1803.00146/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [An Algorithm for Recommending Groceries Based on an Item Ranking Method](https://mlsanity.com/paper/2105.00650/) (2021)
- [UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering](https://mlsanity.com/paper/2308.07048/) (2023)

## Abstract (arXiv)

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.