Online recommendation is an important feature in many applications. In practice, the interaction between the users and the recommender system might be sparse, i.e., the users are not always interacting with the recommender system. For example, some users prefer to sweep around the recommendation instead of clicking into the details. Therefore, a response of zero may not necessarily be a negative response, but a nonresponse. It comes worse to distinguish these two situations when only one item is recommended to the user each time and few further information is reachable. Most existing recommendation strategies ignore the difference between nonresponses and negative responses. In this article, we propose a novel approach to make online recommendations via sparse interactions. We design a contextual bandit algorithm, named hSAOR, for online recommendation. Our method makes probabilistic estimations on whether the user is interacting or not, by reasonably assuming that similar items are similarly attractive. It uses positive and negative responses to build the user preference model, ignoring all nonresponses. Theoretical analyses and experimental results demonstrate its effectiveness.

中文翻译：

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具有隐藏特征的上下文强盗通过稀疏交互进行在线推荐


在线推荐是许多应用程序中的一个重要功能。在实践中，用户和推荐系统之间的交互可能是稀疏的，即用户并不总是与推荐系统交互。例如，一些用户更喜欢浏览推荐而不是点击查看详细信息。因此，零响应不一定是否定响应，而是无响应。当每次只向用户推荐一项并且几乎无法获取更多信息时，区分这两种情况会变得更糟。大多数现有的推荐策略忽略了不回复和负面回复之间的区别。在本文中，我们提出了一种通过稀疏交互进行在线推荐的新颖方法。我们设计了一种上下文老虎机算法，名为 hSAOR，用于在线推荐。我们的方法通过合理地假设相似的项目具有相似的吸引力，对用户是否正在交互进行概率估计。它使用积极和消极的响应来构建用户偏好模型，忽略所有不响应。理论分析和实验结果证明了其有效性。