Motivated by online settings where users can provide explicit feedback about the relevance of products that are sequentially presented to them, we look at the recommendation process as a problem of dynamically optimizing this relevance feedback. Such an algorithm optimizes the fine tradeoff between presenting the products that are most likely to be relevant, and learning the preferences of the user so that more relevant recommendations can be made in the future.
We assume a standard predictive model inspired by collaborative filtering, in which a user is sampled from a distribution over a set of possible types. For every product category, each type has an associated relevance feedback that is assumed to be binary: the category is either relevant or irrelevant. Assuming that the user stays for each additional recommendation opportunity with probability $β$ independent of the past, the problem is to find a policy that maximizes the expected number of recommendations that are deemed relevant in a session.
We analyze this problem and prove key structural properties of the optimal policy. Based on these properties, we first present an algorithm that strikes a balance between recursion and dynamic programming to compute this policy. We further propose and analyze two heuristic policies: a `farsighted' greedy policy that attains at least $1-β$ factor of the optimal payoff, and a naive greedy policy that attains at least $\frac{1-β}{1+β}$ factor of the optimal payoff in the worst case. Extensive simulations show that these heuristics are very close to optimal in practice.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
School of Information, Renmin University of China（中国人民大学信息学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（大数据管理与分析方法北京市重点实验室）；Meituan-Dianping Group（美团点评集团）；Université de Montréal（蒙特利尔大学）
Naveen Jindal School of Management, The University of Texas at Dallas（德克萨斯大学达拉斯分校纳文·金德尔管理学院）；Department of Computer Science and Engineering, The University of North Texas（北得克萨斯大学计算机科学与工程系）
Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks
University of Michigan（密歇根大学）；University of Texas Medical Branch（德克萨斯大学医学部）；Institute for Translational Sciences（转化科学研究所）；Electrical Engineering and Computer Science（电气工程与计算机科学学院）
Naveen Jindal School of Management, The University of Texas at Dallas（德克萨斯大学达拉斯分校纳文·金达尔管理学院）；Department of Computer Science, The University of Texas at Dallas（德克萨斯大学达拉斯分校计算机科学系）
Distributed Online Bandit Submodular Maximization with Bounded Sampling Violations
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化学院）；School of Artificial Intelligence and Automation at the Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）
