Towards Comprehensive Repositories of Opinions
Towards Comprehensive Repositories of Opinions
复制标题
建立综合意见库
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
H. Madhyastha
中科院分区：
文献类型：
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作者：
Han Zhang;Kasra Edalat Nejad;Amir Rahmati;H. Madhyastha
尽管推荐服务（如Yelp、Healthgrades和Angie’s List）很受欢迎，但对于这些服务上列出的大多数实体来说，人们不得不依赖于少数用户分享的意见。我们认为，大多数实体评论的缺乏源于这样一个事实，即绝大多数用户主要消费他人分享的意见，但很少自己发表评论。因此，利用越来越多地从客户端应用程序而不是通过Web访问服务的趋势，我们建议增强推荐服务，以根据对用户活动的观察来隐含地推断任何用户的意见。由于智能手机和可穿戴设备丰富的感官功能，以及现实世界中许多活动留下的数字足迹，对许多用户的建议进行隐性推断是可行的。然而，意见的隐含推理本质上是不确定的，自动分享推理引起了重大的隐私和安全问题。在本文中，我们将讨论如何应对这些挑战，以便寻找推荐的用户能够比目前的情况更全面地利用一组意见。
Despite the popularity of recommendation services (such as Yelp, Healthgrades, and Angie’s List), for a majority of entities listed on these services, one has to rely on opinions shared by a few users. We argue that this paucity of reviews for most entities stems from the fact that the vast majority of users largely consume opinions shared by others but seldom post reviews themselves. Therefore, leveraging the trend that services are increasingly accessed from a client-side app rather than over the Web, we propose augmenting recommendation services to implicitly infer any user’s opinions based on observations of the user’s activities. Implicit inference of many of a user’s recommendations are feasible due to the rich sensory capabilities of smartphones and wearables as well as the digital footprints left behind by many activities in the physical world. However, implicit inference of opinions is inherently uncertain and automated sharing of inferences raises significant privacy and security concerns. In this paper, we discuss how to tackle these challenges so that users looking for recommendations can draw upon a more comprehensive set of opinions than is the case today.