III: Small: Entity Selection and Ranking for Data-Mining Applications

III：小：数据挖掘应用程序的实体选择和排序

• 批准号: 1218437
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218437&HistoricalAwards=false
• 关键词: III; Small; Entity; Selection; Ranking

Expert-management portals like linkedin.com, odesk.com and guru.com are indicative sites that allow people to advertise their work or set of skills to the broader public. For example, linkedin features more than 120 million members which allows potential employers, collaborators, etc. to discover individuals or groups of individuals with the desired expertise. Similarly, review-management sites like Amazon or Yelp collect large number of reviews about products or services. For example, kindle has more than 30,000 reviews on Amazon. Naturally, users cannot go over all these reviews and are helped significantly by the identification of a small subset of reviews that is sufficiently informative. Finally, as online social and media networks grow in importance as sources of news and other information, there is an urgent need for tools that automatically identify and recommend important nodes of the network, that specific users may need to follow to fully exploit the power of online social media. In each of these scenarios, given a collection of entities (e.g., reviews about a product, experts that declare certain skills, network nodes or edges), the goal is to identify a subset of important entities (e.g., useful reviews, competent experts, influential nodes respectively). Existing work on recommender systems attempts to identify important entities either by entity ranking or by entity selection. Entity-ranking methods associate a a score with each entity; They ignore the redundancy between the highly-scored entities. Entity-selection methods try to overcome this drawback by evaluating the desirability of a group of entities taken together; They attempt to identify the best subset of entities, while ignoring other subsets of entities that may be equally-good or almost as good as the best subset. Against this background, this project aims to overcome the drawbacks of existing entity selection and entity ranking methods through a synergistic integration of both into a common framework that allows entity-ranking based on entity selection and entity-selection that based on entity ranking. In the resulting framework, the scores of individual entities are determined in part by the number of good groups of entities they can be part of; and good group of entities consist of entities with high scores. The main challenge addressed by this work is how to explore the solution space of combinatorial problems in order to identify subsets of entities that participate in many good solutions. The resulting new practical methods for exploring the solution space of combinatorial problems find applications related to expert management systems, management of online product reviews, and network analysis (including physical and social networks). The project also offers enhanced opportunities for research-based training of graduate and undergraduate students at Boston University. All of the research results including publications, software, and data will be freely disseminated to the broader research and educational community through the project website at: http://www.cs.bu.edu/~evimaria/sel-and-ranking.html.

专家管理门户网站，如linkedin.com，odesk.com和guru.com是指示性网站，允许人们向更广泛的公众宣传他们的工作或技能。例如，LinkedIn拥有超过1.2亿的会员，这使得潜在的雇主、合作者等能够发现具有所需专业知识的个人或个人团体。类似地，像亚马逊或Yelp这样的评论管理网站收集了大量关于产品或服务的评论。例如，Kindle在亚马逊上有超过30，000条评论。当然，用户不可能浏览所有这些评论，识别一小部分信息量足够大的评论对用户有很大的帮助。最后，随着在线社交和媒体网络作为新闻和其他信息来源的重要性日益增长，迫切需要自动识别和推荐网络重要节点的工具，特定用户可能需要遵循这些工具以充分利用在线社交媒体的力量。在这些场景中的每一个中，给定实体的集合（例如，关于产品的评论、声明某些技能的专家、网络节点或边），目标是识别重要实体的子集（例如，有用的评论、有能力的专家、有影响力的节点）。推荐系统的现有工作试图通过实体排名或实体选择来识别重要实体。可靠性排名方法将a得分与每个实体相关联;它们忽略了得分高的实体之间的冗余。实体选择方法试图通过评估一组实体放在一起的可取性来克服这个缺点;它们试图识别实体的最佳子集，同时忽略可能与最佳子集同等好或几乎一样好的其他实体子集。在此背景下，本项目旨在克服现有的实体选择和实体排名方法的缺点，通过两者的协同集成到一个共同的框架，允许基于实体选择的实体排名和基于实体排名的实体选择。在由此产生的框架中，单个实体的分数部分取决于它们可以成为其中一部分的良好实体组的数量;并且良好实体组由具有高分数的实体组成。这项工作所面临的主要挑战是如何探索组合问题的解决方案空间，以确定参与许多好的解决方案的实体的子集。由此产生的新的实用方法，探索组合问题的解决方案空间，发现与专家管理系统，管理在线产品评论，网络分析（包括物理和社交网络）的应用。该项目还为波士顿大学的研究生和本科生提供更多的研究培训机会。所有的研究成果，包括出版物，软件和数据将通过项目网站免费传播给更广泛的研究和教育界：http://www.cs.bu.edu/~evimaria/sel-and-ranking.html。