Next Generation Applications of Social Systems

社会系统的下一代应用

• 批准号: RGPIN-2014-05093
• 负责人: Lakshmanan, Laks
• 金额: $ 4.52万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566945
• 关键词: Next; Generation; Applications; Social; Systems

Technology has heralded the advent of Online Social Systems such as Social Networks (SN) and Recommender Systems (RS). Data pertaining to these systems--both those that describe them and those that are generated by their users, are essential for research aimed at advancing their state of the art. Recently, increasing availability of real data sets on social systems, and at an unprecedented scale, has facilitated cutting edge research into the design and use of these systems. An important application domain that is common to both SN and RS is marketing. In case of SN research, a major application domain is Viral Marketing (VM). The vision behind VM is to give free samples of a product to a small number of ``seed'' users (consumers) in a SN in the hope that adoption of the product will propagate virally through the network from the seeds to their followers, and their followers, and so on. The seed users chosen must be influential in effecting large cascades of adoption by others. The underlying thesis here is that users are susceptible to the influence of their neighbors: when they observe their neighbors perform an action like buying a product or adopting an innovation, then with some probability, they will be tempted to do the same. Numerous models have been proposed for capturing propagation phenomena which cover many applications including viral marketing, spread of infections, spread of innovation/rumor, etc. In spite of a decade of research with many advances, its penetration into real-world marketing has been modest. RS are the backbone behind the success of companies like Amazon and Netflix: indeed, recommending products to users by leveraging their past profiles has been found to boost product adoption and ultimately sales. RS build profiles on users/items using past user feedback and use the profiles to make future recommendations. The majority of research to date has focused on improving the accuracy of prediction models. While this is important, an equally important business perspective has been largely ignored. In the proposed research, we will focus on this marketing domain and study several fundamental data mining and computational questions that will enable the development of novel next generation applications. Most prior work on VM ignores competition, is based on unrealistic models, ignores the fundamental role of the network owner, or proposes algorithms which do not scale to real SN with hundreds of millions of users. A specific goal of this program is to develop novel models for VM that close the gap between research and ground reality by lifting the above limitations, and design scalable algorithms. We will study novel data mining questions on recommendations motivated by a business perspective: how to make strategic recommendations that optimize expected revenue, while accounting for pricing and boredom effects? how to make efficient and flexible recommendations of not only items but of packages such as travel itineraries, shopping lists, and playlists, with minimal user input? how to recommend events which are evolving or being composed from scratch, to a big collection of users, such as attendees of a large convention? We also intend to study questions at the interplay of SN and RS, such as how to exploit the hidden influence channels in a RS to launch an effective targeted marketing campaign, taking into account complex recommendation models, competition between rival campaigners, and non-monotonic behavior of users who may switch loyalties. Cutting edge research fueled by these questions will drive the technology forward and just as importantly, take both viral or network-driven marketing and recommendation-driven marketing much closer to reality, leading to industry-strength systems and applications.

技术已经预示了在线社交系统的到来，例如社交网络（SN）和推荐系统（RS）。与这些系统有关的数据-无论是描述这些系统的数据，还是由其用户生成的数据，对于旨在推进其最新技术水平的研究都是必不可少的。最近，社会系统上真实的数据集的可用性越来越高，而且规模空前，这促进了对这些系统的设计和使用的前沿研究。SN和RS共同的一个重要应用领域是营销。在SN研究的情况下，一个主要的应用领域是病毒式营销（VM）。VM背后的愿景是将产品的免费样品提供给SN中的少数“种子”用户（消费者），希望产品的采用将通过网络从种子传播到他们的追随者，以及他们的追随者，等等。这里的基本论点是，用户容易受到邻居的影响：当他们观察到邻居执行购买产品或采用创新等行为时，他们很可能会受到诱惑做同样的事情。已经提出了许多模型来捕捉传播现象，其中包括病毒式营销，感染传播，创新/谣言传播等许多应用，尽管十年的研究取得了许多进展，但其对现实世界营销的渗透一直是适度的。RS是亚马逊和Netflix等公司成功背后的支柱：事实上，通过利用用户过去的个人资料向用户推荐产品已被发现可以促进产品的采用并最终促进销售。RS使用过去的用户反馈构建用户/项目的配置文件，并使用这些配置文件来提出未来的建议。迄今为止，大多数研究都集中在提高预测模型的准确性上。虽然这很重要，但同样重要的商业视角在很大程度上被忽视了。在拟议的研究中，我们将专注于这个营销领域，并研究几个基本的数据挖掘和计算问题，这将使新的下一代应用程序的开发。大多数关于VM的现有工作忽略了竞争，基于不切实际的模型，忽略了网络所有者的基本作用，或者提出了不能扩展到具有数亿用户的真实的SN的算法。该计划的一个具体目标是开发新的VM模型，通过解除上述限制来缩小研究与地面现实之间的差距，并设计可扩展的算法。我们将研究新的数据挖掘问题的建议动机的商业角度：如何使战略建议，优化预期收入，同时考虑到定价和无聊的影响？如何以最少的用户输入，不仅对项目而且对诸如旅行路线、购物列表和播放列表之类的包进行高效和灵活的推荐？如何向大量用户（例如大型会议的与会者）推荐正在发展或从头开始编写的事件？我们还打算研究SN和RS相互作用的问题，例如如何利用RS中隐藏的影响渠道来发起有效的有针对性的营销活动，同时考虑到复杂的推荐模型，竞争对手之间的竞争，以及可能切换忠诚度的用户的非单调行为。由这些问题推动的前沿研究将推动技术向前发展，同样重要的是，使病毒式或网络驱动的营销和广告驱动的营销更接近现实，从而产生行业优势的系统和应用程序。