In recent years, predictions of retweet behavior have attracted growing attention from both academic and industrial fields since the retweets can help with a number of tasks such as hot topic detection and personalized tweet recommendations. However, existing studies on predictions of retweet behavior have not taken full advantage of the effects of multiple social relationships between users. This paper presents a probabilistic model which incorporates multiple trust relationships between users into a traditional Bayesian Poisson factorization (BPF) model to predict retweet behavior. This model can capture the effects of a variety of social influences and integrates user trust strengths into the framework. Tests on the Sina Weibo dataset demonstrate that the model gives 90.91% improvement in the NDCG@3 score and 88.37% improvement in the precision@3 score over a traditional BPF.
WANG Shaoqing
,
LI Cuiping
,
WANG Zheng
,
CHEN Hong
. Prediction of retweet behavior based on multiple trust relationships[J]. Journal of Tsinghua University(Science and Technology), 2019
, 59(4)
: 270
-275
.
DOI: 10.16511/j.cnki.qhdxxb.2018.25.060
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