A Dynamic Users' Interest Discovery Model with Distributed Inference Algorithm

作者：Xu, Shuo; Shi, Qingwei; Qiao, Xiaodong^*; Zhu, Lijun; Zhang, Han; Jung, Hanmin; Lee, Seungwoo; Choi, Sung-Pil

来源：INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2014, 10(4): 280892.

DOI：10.1155/2014/280892

One of the key issues for providing users user-customized or context-aware services is to automatically detect latent topics, users' interests, and their changing patterns from large-scale social network information. Most of the current methods are devoted either to discovering static latent topics and users' interests or to analyzing topic evolution only from intrafeatures of documents, namely, text content, without considering directly extrafeatures of documents such as authors. Moreover, they are applicable only to the case of single processor. To resolve these problems, we propose a dynamic users' interest discovery model with distributed inference algorithm, named as Distributed Author-Topic over Time (D-AToT) model. The collapsed Gibbs sampling method following the main idea of MapReduce is also utilized for inferring model parameters. The proposed model can discover latent topics and users' interests, and mine their changing patterns over time. Extensive experimental results on NIPS (Neural Information Processing Systems) dataset show that our D-AToT model is feasible and efficient.

• 出版日期2014
• 单位东北师范大学; 辽宁工程技术大学; 中国科学技术信息研究所