Affiliations: [a] Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei, Anhui, China | [b] School of Computer science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China | [c] School of Computer and Information Engineering, Chuzhou University, Chuzhou, Anhui, China | [d] School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu, China
Correspondence:
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Corresponding author: Lei Li, Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei, Anhui 230009, China. E-mail: lilei@hfut.edu.cn.
Abstract: Inferring user interest over large-scale microblogs have attracted much attention in recent years. However, the emergence of the massive data, dynamic change of information and persistence of microblogs pose challenges to interest inference. Most of the existing approaches rarely take into account the combination of these microbloggers’ characteristics within the model, which may incur information loss with nontrivial magnitude in real-time extraction of user interest and massive social data processing. To address these problems, in this paper, we propose a novel User-Networked Interest Topic Extraction in the form of Subgraph Stream (UNITE_SS) for microbloggers’ interest inference. To be specific, we develop several strategies for the construction of subgraph stream to select the better strategy for user interest inference. Moreover, the information of microblogs in each subgraph is utilized to obtain a real-time and effective interest for microbloggers. The experimental evaluation on a large dataset from Sina Weibo, one of the most popular microblogs in China, demonstrates that the proposed approach outperforms the state-of-the-art baselines in terms of precision, mean reciprocal rank (MRR) as well as runtime from the effectiveness and efficiency perspectives.
Keywords: Information processing, microblog, social network, subgraph stream, user interest inference
