Contextual Bandits With Hidden Features to Online Recommendation via Sparse Interactions

Shangdong Yang, Hao Wang, Chenyu Zhang, Yang Gao

Abstract: 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.