决策理论交互式视频顾问
The Decision-Theoretic Interactive Video Advisor
摘要：
为了帮助人们在大量的项目中进行选择，并通过大量的信息进行过滤，导致了大量的研究在建设个人推荐代理。在构建这样的代理的中心问题之一是表示和用户的喜好或兴趣的启发。这个主题在决策理论中已经研究了很长时间，但令人惊讶的是，在推荐系统领域很少有工作使用正式的决策理论技术。本文介绍了DIVA，一个决策理论代理推荐电影，包含了一些新的功能。DIVA使用项目之间的成对比较来表示用户偏好，而不是数字评级。它使用了一种新的相似性度量的基础上的概念，两个排序的项目之间的冲突的概率。该系统具有丰富的偏好表示，区分用户对电影的一般品味和他的直接兴趣。它采用了一种增量的方法来获取偏好，如果用户对推荐列表不满意，可以提供反馈。我们经验评估系统的性能，使用EachMovie协同过滤数据库。
英文摘要：
The need to help people choose among large numbers of items and to filter through large amounts of information has led to a flood of research in construction of personal recommendation agents. One of the central issues in constructing such agents is the representation and elicitation of user preferences or interests. This topic has long been studied in Decision Theory, but surprisingly little work in the area of recommender systems has made use of formal decision-theoretic techniques. This paper describes DIVA, a decision-theoretic agent for recommending movies that contains a number of novel features. DIVA represents user preferences using pairwise comparisons among items, rather than numeric ratings. It uses a novel similarity measure based on the concept of the probability of conflict between two orderings of items. The system has a rich representation of preference, distinguishing between a user's general taste in movies and his immediate interests. It takes an incremental approach to preference elicitation in which the user can provide feedback if not satisfied with the recommendation list. We empirically evaluate the performance of the system using the EachMovie collaborative filtering database.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
