In many recommender problems, a handful of popular items (e.g. movies/TV shows, news etc.) can be dominant in recommendations for many users. However, we know that in a large catalog of items, users are likely interested in more than what is popular. The dominance of popular items may mean that users will not see items they would likely enjoy. In this paper, we propose a technique to overcome this problem using adversarial machine learning. We define a metric to translate user-level utility metric in terms of an advantage/disadvantage over items. We subsequently use that metric in an adversarial learning framework to systematically promote disadvantaged items. The resulting algorithm identifies semantically meaningful items that get promoted in the learning algorithm. In the empirical study, we evaluate the proposed technique on three publicly available datasets and four competitive baselines. The result shows that our proposed method not only improves the coverage, but also, surprisingly, improves the overall performance.

翻译：在许多推荐问题中，少数热门项目（如电影/电视节目、新闻等）可能主导多数用户的推荐结果。然而我们知道，在庞大的项目目录中，用户可能对热门项目之外的内容同样感兴趣。热门项目的支配地位可能导致用户无法看到他们可能喜欢的项目。本文提出一种利用对抗机器学习克服该问题的技术。我们定义了一种度量标准，将用户层面的效用度量转化为项目层面的优势/劣势指标，进而将该度量融入对抗学习框架中，系统性地推广处于劣势的项目。最终算法能够识别具有语义意义的项目，并在学习过程中对其加以推广。实验研究中，我们在三个公开数据集上与四个强基线方法进行对比评估。结果表明，本文提出的方法不仅提升了推荐覆盖率，更令人惊讶的是，还全面提升了整体性能。