CAREER: On the identification of collections with complex objectives

职业：关于识别具有复杂目标的藏品

• 批准号: 1253393
• 负责人: Evimaria Terzi
• 金额: $ 50万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1253393&HistoricalAwards=false
• 关键词: CAREER; identification; collections; complex; objectives

In many domains, there is an increasing reliance on Recommender Systems for helping identify products, services and people that meet some user-specified criteria. Given a pool of entities (e.g., movies, books, experts) and an objective function such systems have to identify a collection (i.e., a subset) of entities from the pool that optimizes the objective function. For example, in movie-recommendation systems (e.g., Netflix) the goal is to identify subsets of movies to recommend to registered users. Analogous problems arise in social networks and social media (e.g., Twitter, Facebook), where advertisers need to identify a small set of targets for their advertisements. Finally, project management teams in large organizations often use expertise management systems to identify the subset of experts needed to complete a specific project.Current Recommender Systems suffer from severe limitations in settings where (i) the users multiple interactions with the system over time and the recommendations provided to a specific user at any given time need to take into account the past recommendations given to the same user or (ii) The entities that make up the recommended collections are rational entities, e.g., participants in a social network, or members of a project team, that have their own goals and preferences that influence their behavior as members of the collection. This project aims to address these two shortcomings of current Recommender Systems by designing, implementing, and evaluating combinatorial algorithms for identifying (a) sequences of collections, rather than a single collection and (b) collections of rational entities with individual goals, preferences, or objectives.In addition to developing a suite of novel combinatorial algorithms and heuristics for recommending sequences of collections and collections of rational entities, the project aims to develop and deploy two application-specific testbeds: (i) a personalized meal planner provides to its users weekly meal recommendations to guide them towards healthy eating choices; and (ii) A crowdsourcing platform with support for virtual team formation to allow students registered in some of the courses at Boston University, to form teams online to collaborate on class projects (when appropriate). Broader impacts of this research include: new models and methods that signficantly advance the current state of the art in Recommender Systems, with broad applications in a number of domains including social networks (e.g., LinkedIn, Facebook, etc.), online recommendation systems (e.g., Amazon, Netflix, etc.), and daily-deal sites (e.g., Groupon, LivingSocial, etc.). The project contributes to the education and advanced research-based training of graduate and undergraduate students in Computer Science at Boston University. Wide dissemination of software implementations of the algorithms can be expected to benefit the larger research community.Additional information about the project, including links to project personnel, publications, and software can be found at: http://www.cs.bu.edu/~evimaria/recommendations.html

在许多领域，人们越来越依赖于推荐系统来帮助识别满足某些用户指定标准的产品、服务和人员。给定一个实体池（例如，电影，书籍，专家）和一个目标函数，这样的系统必须从优化目标函数的池中识别实体的集合（例如，一个子集）。例如，在电影推荐系统（例如Netflix）中，目标是识别要推荐给注册用户的电影子集。类似的问题也出现在社交网络和社交媒体（如Twitter、Facebook）中，广告商需要为他们的广告确定一小部分目标群体。最后，大型组织中的项目管理团队经常使用专业知识管理系统来确定完成特定项目所需的专家子集。当前的推荐系统在以下设置中存在严重的局限性：(i)用户随着时间的推移与系统进行多次交互，并且在任何给定时间向特定用户提供的推荐需要考虑到过去向同一用户提供的推荐；（ii）组成推荐集合的实体是理性实体，例如，社交网络的参与者或项目团队的成员。它们都有自己的目标和偏好，这些目标和偏好会影响它们作为集合成员的行为。本项目旨在通过设计、实现和评估组合算法来解决当前推荐系统的这两个缺点，这些组合算法用于识别(a)集合序列，而不是单个集合和(b)具有单个目标、偏好或目标的理性实体集合。除了开发一套用于推荐集合序列和理性实体集合的新颖组合算法和启发式方法外，该项目还旨在开发和部署两个特定于应用程序的测试平台：(i)个性化膳食计划向其用户提供每周膳食建议，以指导他们做出健康的饮食选择；（ii）支持虚拟团队组建的众包平台，允许在波士顿大学注册某些课程的学生在线组建团队，在适当的情况下进行课堂项目合作。本研究的更广泛影响包括：新模型和方法显著推进了推荐系统的当前状态，广泛应用于许多领域，包括社交网络（例如，LinkedIn， Facebook等），在线推荐系统（例如，亚马逊，Netflix等）和日常交易网站（例如，Groupon， LivingSocial等）。该项目为波士顿大学计算机科学研究生和本科生的教育和高级研究培训做出了贡献。这些算法的软件实现的广泛传播有望使更大的研究团体受益。有关该项目的更多信息，包括项目人员、出版物和软件的链接，可在http://www.cs.bu.edu/~evimaria/recommendations.html上找到