This paper discusses the use of portfolio approaches based on bandit algorithms to optimize
multicriteria decision-making in recommender systems (accuracy and diversity). While …

We investigate the problem of stochastic, combinatorial multi-armed bandits where the
learner only has access to bandit feedback and the reward function can be non-linear. We …

Exposure bias is a well-known issue in recommender systems where items and suppliers
are not equally represented in the recommendation results. This bias becomes particularly …

Cascading bandits is a natural and popular model that frames the task of learning to rank
from Bernoulli click feedback in a bandit setting. For the case of unstructured rewards, we …

The interactive recommender systems involve users in the recommendation procedure by
receiving timely user feedback to update the recommendation policy. Therefore, they are …

Relevance ranking and result diversification are two core areas in modern recommender
systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item …

This paper addresses the cold-start problem in online learning to rank (OLTR). We show
both theoretically and empirically that priors improve the quality of ranked lists presented to …

The linear submodular bandit problemwas proposedto simultaneously address diversified
retrieval and online learning in a recommender system. If there is no uncertainty, this …

Many sequential decision making tasks can be viewed as combinatorial optimiza-tion
problems over a large number of actions. When the cost of evaluating an ac-tion is high …

Recurrent neural networks have proven effective in modeling sequential user feedbacks for
recommender systems. However, they usually focus solely on item relevance and fail to …