We model the interaction between a user and an AI driven recommendation system. The user initiates the process by conveying preference information through a costly and noisy message. The AI assistant, acting as a Bayesian agent, interprets the user's message to form a posterior belief about their true preferences and make product recommendations. In particular, it determines how many recommendations to present so as to maximize the user's expected utility from their final choice, while accounting for the search cost induced by the size of the recommendation set. We use mutual information based cost functions to model the two distinct costs incurred by the user during the interaction: (i) a communication cost, which increases with the precision of their preference message, and (ii) a search cost, which increases with the size of the recommendation set provided by the AI assistant.
We study products and preferences which live in d dimensional space, and ask how the user's expected payoff can be maximized. For large d, we characterize how optimal message precision and recommendation set size depend on the cost parameters, under two distinct distributions from which recommendations can be sampled from the product universe: (i) Bayes' posterior belief, and (ii) an optimized tilted distribution. Under the posterior sampling scheme (i), we identify a hybrid regime, in which an efficient interaction policy requires jointly optimizing the amount of information (in bits) conveyed by the user and the number of recommendations provided by the AI assistant. In the tilted sampling scheme (ii), our results show that the optimal interaction policy uses only one of communication and search, favoring whichever of them is less costly.
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Department of Communication, University of California, Los Angeles, CA 90095（通信系，加州大学洛杉矶分校，CA 90095）；Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104（计算机与信息科学系，宾夕法尼亚大学，Philadelphia, PA 19104）；Amenenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104（安纳伯格通信学院，宾夕法尼亚大学，Philadelphia, PA 19104）；Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA 19104（运营、信息与决策系，宾夕法尼亚大学，Philadelphia, PA 19104）
Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国立医学图书馆）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
The Chinese University of Hong Kong（香港中文大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Beijing Institute of Technology（北京理工大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations
University of Siegen（锡根大学）；Institute for Knowledge-Based Systems and Knowledge Management（知识与知识管理研究所）；Institute for Embedded Systems（嵌入式系统研究所）；Department of Digital Health Sciences and Biomedicine（数字健康科学与生物医学系）
End-to-end Learnable Clustering for Intent Learning in Recommendation
Ant Group（蚂蚁集团）；National University of Singapore（新加坡国立大学）；Westlake University（西湖大学）；Alibaba Group（阿里巴巴集团）；National University of Defense Technology（国防科技大学）；Zhejiang University（浙江大学）
Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach
Computer Science, Utrecht University, Utrecht, The Netherlands Michel Chaudron Department of Mathematics；Computer Science, TU Eindhoven, Eindhoven, The Netherlands
AI, Help Me Think$\unicode{x2014}$but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support
Microsoft Research Cambridge（微软研究院剑桥分部）；University College London（伦敦大学学院）；fortiss GmbH, Research Institute of the Free State of Bavaria（巴伐利亚自由州fortiss GmbH研究机构）；LMU Munich（慕尼黑大学）；Politecnico di Milano（米兰理工大学）
