Time-aware Community-enhanced Social Information Retrieval

时间感知社区增强社交信息检索

• 批准号: RGPIN-2021-03170
• 负责人: Fani, Hossein
• 金额: $ 2.11万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742413
• 关键词: Time; aware; Community; enhanced; Social

Search engines are the foremost means of information retrieval (IR) in the modern era, yet they have difficulty searching into knowledge repositories. This challenge arises not only due to the four V's of big data but also because they are not tailored to the users' differing information needs at different points in time. On the other hand, online social network (OSN) platforms are emerging as an indispensable means of communication, allowing for the seamless flow of information through user interaction (microscopic), which can result in viral spread effects (macroscopic). Social IR is to ease IR systems by integrating additional social network contexts for enhancing search engine rankings. Successfully as they are, Social IR methods, however, face three significant challenges which impede the optimum synergistic integration of OSN into IR: i) granularity: thus far, Social IR methods have been integrating social context at the fine-grain user level. As such, they are not scalable to a large number of users. Also, not all users actively contribute in OSNs; hence, they lack a rich social context that renders the user-level modelings unreliable and moot. Further, studies at the user level raise privacy concerns due to their intrusive approaches. Collective use of social information is also error-prone since it overlooks the locally dense communal inter-user associations, ii) temporality: user's information needs and her subjective definition of relevance in terms of topicality, novelty, reliability, understandability, importance, and scope are changing within time. While temporality has been well-explored in IR and social network analysis, the impacts of temporal OSN on IR processes has not explored yet, iii) balance: although propagating social information into the IR processes can enhance retrieval performance, Social IR may not always hold promise, e.g., when the search results solely rely on and overfit to the user's social model as opposed to her information needs. The overarching theme of my research is to study the possibility of developing temporal community-based Social IR techniques that would benefit from the OSNs for enhancing the search efficacy and efficiency. This proposal is original and novel because my students and I are the first, to the best of our knowledge, who fill the gap in Social IR by considering community structures and temporal dynamics in the OSNs as well as their synergistic trade-offs on IR processes via 1) scalable, 2) reliable, 3) non-intrusive, and 4) temporal methods, which would lead to a next-generation of IR technologies. The proposed research will provide a solid foundation for the training of HQP, including 3 Ph.D., 6 M.Sc., and 5 undergraduate students, who will acquire training in and become adept at social network analysis and information retrieval. The HQP can immediately be absorbed by the lively and growing data science and the analytics job market in Canada.

搜索引擎是现代最重要的信息检索手段，但它们在搜索知识库方面存在困难。这一挑战的出现不仅是由于大数据的四个V，而且还因为它们没有针对用户在不同时间点的不同信息需求进行定制。另一方面，在线社交网络（OSN）平台正在成为一种不可或缺的通信手段，允许通过用户交互（微观）实现信息的无缝流动，这可能导致病毒式传播效应（宏观）。社交IR是通过整合额外的社交网络上下文来简化IR系统，以提高搜索引擎排名。然而，尽管它们是成功的，但社会IR方法面临三个重大挑战，这阻碍了OSN到IR的最佳协同集成：i）粒度：到目前为止，社会IR方法一直在细粒度用户级别集成社会上下文。因此，它们无法扩展到大量用户。此外，并非所有用户都积极参与OSN;因此，他们缺乏丰富的社会背景，使得用户级建模不可靠且无实际意义。此外，在用户层面的研究提出了隐私问题，由于其侵入性的方法。集体使用的社会信息也是容易出错的，因为它忽略了当地密集的社区用户间的协会，二）暂时性：用户的信息需求和她的主观定义的相关性的话题性，新奇性，可靠性，可理解性，重要性和范围内的时间正在发生变化。虽然在IR和社会网络分析中已经很好地探索了时间性，但时间OSN对IR过程的影响尚未探索，iii）平衡：尽管将社会信息传播到IR过程中可以提高检索性能，但社会IR可能并不总是有希望，例如，当搜索结果仅仅依赖于用户的社会模型，而不是她的信息需求时。我的研究的首要主题是研究开发时间的社区为基础的社会IR技术，将受益于OSNs提高搜索效率和效率的可能性。这个建议是原创和新颖的，因为我的学生和我是第一个，据我们所知，谁填补了社会IR的差距差距，通过考虑社区结构和时间动态的OSN以及他们的协同权衡IR过程通过1）可扩展的，2）可靠的，3）非侵入性的，和4）时间的方法，这将导致下一代的IR技术。本研究将为培养包括3名博士在内的高素质人才奠定坚实的基础，6名硕士，以及5名本科生，他们将接受社会网络分析和信息检索的培训并熟练掌握。HQP可以立即被加拿大活跃和不断增长的数据科学和分析就业市场所吸收。