Paper Info
Title
Joint Topic-Semantic-aware Social Matrix Factorization for online voting recommendation
Abstract
Social voting is an emerging new feature in online social platforms, through which users can express their attitudes and opinions towards various interested subjects. Since both social relations and textual content decide the votes propagation, the diverse sources present opportunities and challenges for recommender systems. In this paper, we jointly consider these two factors for the online voting recommendation. First, we conduct feature learning on the vote content. Note that the vote questions are usually short and contain informal expressions, existing text mining methods cannot handle it well. We propose a novel topic-enhanced word embedding (TEWE) method, which learns the word vectors by considering both token-level semantics and document-level mixture topics. Second, we propose two Joint Topic-Semantic-aware Social Matrix Factorization (JTS-MF) models, which fuse social relations and textual content for the vote recommendation. Specifically, JTS-MF1 directly identifies the interaction strength to calculate the similarity among users and votes, while JTS-MF2 aims to preserve inter-user and inter-vote similarities during matrix factorization. Extensive experimental results on real online voting dataset show the effectiveness of our approaches against several state-of-the-art baselines. JTS-MF1 and JTS-MF2 models surpass the matrix factorization based method, with 25.4% and 57.1% improvements in the top-1 recall, and 59.12% and 25.1% improvements in the top-10 recall.
Year
DOI
Venue
2020
10.1016/j.knosys.2020.106433
Knowledge-Based Systems
Keywords
DocType
Volume
Online voting,Recommender systems,Topic-enhanced word embedding,Matrix factorization
Journal
210
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jia Wang17917.75
Hongwei Wang240718.52
Miao Zhao377236.57
Jiannong Cao45226425.12
Zhuo Li52811.11
Minyi Guo63969332.25