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.
翻译:许多建议系统都受到受欢迎偏差问题的影响:人们经常推荐受欢迎的产品,而不太受欢迎的利基产品则很少甚至很少推荐,然而,这些被忽视的产品正是企业需要找到客户的产品,它们的建议将更有益。 在本文件中,我们研究了一种项目加权办法,以改进长尾建议。 我们的方法是对现有建议算法进行简单而有力的补充,以便在准确性和长尾覆盖之间作出可加权衡的权衡。