Popularity-Aware Item Weighting for Long-Tail Recommendation
This addresses the issue for businesses needing to recommend less popular, niche products, but it is incremental as it builds on existing algorithms.
The paper tackles the popularity bias problem in recommender systems by proposing an item weighting approach to improve long-tail recommendation, resulting in a tunable trade-off between accuracy and long-tail coverage.
Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this paper, we examine an item weighting approach to improve long-tail recommendation. Our approach works as a simple yet powerful add-on to existing recommendation algorithms for making a tunable trade-off between accuracy and long-tail coverage.