CAREER: On the identification of collections with complex objectives

在许多领域，越来越依赖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）和这样的系统必须识别集合的目标函数（即，子集）来优化目标函数。例如，在电影推荐系统（例如，Netflix）的目标是确定电影的子集，以推荐给注册用户。 类似的问题出现在社交网络和社交媒体中（例如，Twitter、Facebook），广告商需要为他们的广告确定一小部分目标。最后，当前的推荐系统在以下设置中受到严重的限制：（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提供给特定用户的推荐需要考虑过去给予同一用户的推荐，或者（ii）组成推荐集合的实体是理性实体，例如，社交网络中的参与者或项目团队的成员，他们有自己的目标和偏好，这些目标和偏好会影响他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，该组合算法用于识别（a）集合序列，而不是单个集合，以及（B）具有个人目标、偏好、除了开发一套新颖的组合算法和算法来推荐集合和集合的序列之外，该项目旨在开发和部署两个针对具体应用的试验平台：㈠个性化膳食计划者向其用户提供每周膳食建议，指导他们选择健康饮食;及（ii）一个支援虚拟团队组建的众包平台，让注册波士顿大学部分课程的学生可在网上组成团队，就课堂项目进行合作（如适用）。 这项研究的更广泛的影响包括：新的模型和方法，显着推进推荐系统的当前技术水平，在许多领域，包括社交网络（例如，LinkedIn、Facebook等），在线推荐系统（例如，亚马逊、Netflix等），和每日交易站点（例如，Groupon、LivingSocial等）。该项目有助于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。算法的软件实现的广泛传播有望使更大的研究社区受益。有关该项目的其他信息，包括项目人员、出版物和软件的链接，请访问：http://www.cs.bu.edu/~evimaria/recommendations.html

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