图书情报知识 ›› 2017, Vol. 0 ›› Issue (3): 54-60.doi: 10.13366/j.dik.2017.03.054

• 情报、信息与共享 • 上一篇    下一篇

基于多维情景的移动社交网络用户偏好获取研究

张继东,杨杨   

  • 出版日期:2017-05-10 发布日期:2017-05-10

Research of User Preference Acquisition Based on MultiDimensional Scenario in Mobile Social Network

  • Online:2017-05-10 Published:2017-05-10

摘要:

随着移动社交网络的迅速发展,“移动信息过载”问题随之产生,移动社交网络服务中大量复杂的移动信息导致用户个性化服务需求被覆盖。为了实现对移动社交网络个性化用户偏好的及时、准确预测,本文结合上下文感知模型提出一种基于多维情景的移动社交网络用户偏好获取方法。首先,从上下文信息、用户认知行为、服务质量多维情景出发,分别提取基于有效上下文、基于用户有效认知和基于服务质量属性的用户偏好;其次,融合以上情景用户偏好构建移动社交网络用户偏好感知模型;最后,采用层次向量空间表示法来描述用户偏好模型,并通过基于用户反馈信息的更新处理机制完成对模型的更新。该模型实现了对移动社交网络用户偏好的快速预测、动态追踪和及时更新,提高了用户偏好预测的准确性,更好地满足移动社交网络用户的个性化信息需求。

关键词: 多维情景, 移动社交网络, 用户偏好, 上下文, 认知行为

Abstract:

With the rapid development of mobile social network, the problem of “mobile information overload” arises at the historic moment, a lot of complex mobile information causes users’ personalized service needs to be covered in mobile social networking services. In order to make a timely and accurate prediction of personalized user preference in mobile social network, we propose a method to acquire user preference based on multi-dimensional scenarios and a contextawareness model of mobile social networks. Firstly, according to the multidimensional scenarios of context, user cognitive behavior and quality of service, we respectively extract user preferences based on valid context, user’s effective recognition and the quality attribute of the service. Then, we construct a user-awareness model of mobile social network by fusing the multi-dimensional user preference. Finally, we use the method of hierarchical vector space to describe the user preference model, and update the model through update-processing mechanism based on the user-feedback information. The model has realized the quick prediction, dynamic tracking and updating of the user preferences in mobile social network, the accuracy of user preference prediction has been improved, which makes user personalized information needs to be better satisfied in mobile social network.

Keywords: Multi-dimensional scenario, Mobile social networking, User preference, Context, Cognitive behavior