Actionable Behaviour Discovery in Heterogeneous Social Graphs

异构社交图中可操作的行为发现

• 批准号: RGPIN-2020-05148
• 负责人: Davoudi, Heidar
• 金额: $ 1.75万
• 依托单位: University of Ontario Institute of Technology
• 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=740413
• 关键词: Actionable; Behaviour; Discovery; Heterogeneous; Social

Today, organizations and companies have accumulated online user activity data on unprecedented scales. Such data has become ubiquitous everywhere, ranging from online news agencies to e-commerce retailers. Mining and understanding user behaviour have crucial roles in making better business decisions, improving the quality of service, and enhancing user experience in many domains. Thus, there is a growing demand for leveraging the full potential of available data to generate practical and actionable intelligence. In the real world, users are connected (e.g., friendship in social media) and interact with different objects (e.g., news, products). These objects are usually in the form of unstructured text, which is hard to mine/analysis, and have relationships with other objects, carrying rich semantic meaning. For instance, a user may like a news article because it contains two celebrities involved in a particular event. Moreover, users and the environment (e.g., relationships) keep changing over time. Traditional approaches for user behaviour modelling overlook these complexities by pruning involved objects, ignoring their rich semantic relationships, and focusing on static data. Moreover, the simplicity of current model outputs (e.g., subscription prediction) does not lead to actionable intelligence (e.g.,  reasons behind subscription) in many scenarios. The long-term goal of the research program is to develop an end-to-end framework for building and inferring actionable knowledge from heterogeneous and dynamic user activity data. The framework is built upon the notion of heterogeneous social graphs, graphs with different types of nodes and links, including users, objects and their relationships. The goal is to develop theories, techniques, and tools that make the mining process simple and flexible in order to be applied to a wide range of applications in heterogeneous social graphs. The short-term objectives of the proposed research program are identified as follows. We seek to mine user behaviour in text enriched heterogeneous social graphs. Furthermore, we study the models which capture temporal dynamics in dynamic heterogeneous social graphs. In particular, we focus on developing actionable intelligence discovery in heterogeneous social graphs by designing transparent, explainable models. The anticipated outcomes of our research are: (i) a framework for building and mining of large-scale dynamic heterogeneous graphs, built from text enriched user activity data, (ii) novel algorithms, techniques, and tools that address the lack of actionability in user behaviour discovery techniques. The proposed program is of broad industrial interest as it makes sense of online user activity data, and consequently, paves the way for numerous impactful applications in a wide range of social, economic, and environmental issues. It contributes to training the demanding High Qualified Personnel (HQP) and boosts innovation in products/services across Canada.

如今，组织和公司以前所未有的规模积累了在线用户活动数据。这些数据已经无处不在，从在线新闻机构到电子商务零售商。挖掘和理解用户行为在做出更好的业务决策、提高服务质量和增强许多领域的用户体验方面发挥着至关重要的作用。因此，越来越需要充分利用可用数据的潜力来生成实用和可操作的情报。 在真实的世界中，用户是连接的（例如，社交媒体中的友谊）并与不同对象交互（例如，新闻，产品）。这些对象通常以非结构化文本的形式存在，难以挖掘/分析，并且与其他对象存在关系，承载着丰富的语义。例如，用户可能喜欢新闻文章，因为它包含涉及特定事件的两个名人。此外，用户和环境（例如，关系）随着时间的推移而不断变化。传统的用户行为建模方法忽略了这些复杂性，通过修剪涉及的对象，忽略它们丰富的语义关系，并专注于静态数据。此外，当前模型输出的简单性（例如，预订预测）不会导致可操作的智能（例如， 订阅背后的原因）在许多情况下。该研究计划的长期目标是开发一个端到端的框架，用于从异构和动态的用户活动数据中构建和推断可操作的知识。该框架建立在异构社会图的概念上，即具有不同类型的节点和链接的图，包括用户，对象及其关系，其目标是开发理论，技术和工具，使挖掘过程简单而灵活，以便应用于异构社会图的广泛应用。 拟议研究计划的短期目标确定如下。我们寻求挖掘用户行为的文本丰富的异构社交图。此外，我们还研究了在动态异构社会图中捕捉时间动态的模型。特别是，我们专注于通过设计透明，可解释的模型在异构的社交图中开发可操作的智能发现。我们的研究的预期成果是：（i）一个框架，用于构建和挖掘大规模的动态异构图，从文本丰富的用户活动数据，（ii）新的算法，技术和工具，解决缺乏actionability在用户行为发现技术。该计划具有广泛的工业利益，因为它使在线用户活动数据变得有意义，因此，为广泛的社会，经济和环境问题中的许多有影响力的应用铺平了道路。它有助于培训高素质人才（HQP），并促进加拿大各地产品/服务的创新。