Time-aware Community-enhanced Social Information Retrieval

搜索引擎是现代信息检索的最主要手段，但它们在搜索知识库方面存在困难。这一挑战的出现不仅源于大数据的四个V，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量身定做。另一方面，在线社交网络(OSN)平台正在崛起，成为一种不可或缺的沟通手段，允许通过用户交互(微观)实现信息的无缝流动，这可能会导致病毒传播效应(宏观)。社交IR是通过整合额外的社交网络上下文来提高搜索引擎排名，从而简化IR系统。然而，尽管社会信息检索方法取得了成功，但它们面临着三个重大挑战，这些挑战阻碍了OSN与信息检索的最佳协同整合：i)粒度：到目前为止，社会信息检索方法一直在细粒度的用户级别整合社会背景。因此，它们不能扩展到大量用户。此外，并不是所有的用户都在OSN中积极做出贡献；因此，他们缺乏丰富的社交环境，这使得用户级建模不可靠和没有意义。此外，用户层面的研究由于其侵入性的方法而引起了对隐私的担忧。社会信息的集体使用也容易出错，因为它忽略了局部密集的公共用户间关联：ii)时间性：用户的信息需求及其对相关性的主观定义在主题性、新颖性、可靠性、可理解性、重要性和范围方面随着时间的推移而变化。虽然时态在信息检索和社会网络分析中已经得到了很好的研究，但时态OSN对信息检索过程的影响还没有被探索，iii)平衡：虽然将社会信息传播到信息检索过程中可以提高检索性能，但社会信息检索并不总是有希望的，例如，当搜索结果完全依赖于用户的社交模型，而不是用户的信息需求时。我研究的主要主题是研究开发基于时态社区的社会信息检索技术的可能性，这些技术将从OSN中受益，以提高搜索效率和效率。这一建议是原创和新颖的，因为据我们所知，我和我的学生是第一个通过考虑OSN中的社区结构和时间动态以及他们在IR过程中的协同权衡来填补社会IR空白的人，通过1)可扩展的、2)可靠的、3)非侵入性的和4)将导致下一代IR技术的方法。拟议的研究将为HQP的培训提供坚实的基础，包括3名博士、6名硕士和5名本科生，他们将接受社会网络分析和信息检索方面的培训并变得熟练。HQP可以立即被活跃且不断增长的数据科学和加拿大的分析就业市场所吸收。

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.