کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
484099 | 703253 | 2016 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: RTPMF: Leveraging User and Message Embeddings for Retweeting Behavior Prediction RTPMF: Leveraging User and Message Embeddings for Retweeting Behavior Prediction](/preview/png/484099.png)
Understanding retweeting mechanism and predicting retweeting behavior is an important and valuable task in user behavior analysis. In this paper, aiming at providing a general method for improving retweeting behavior prediction performance, we propose a probabilistic matrix factorization model (RTPMF) incorporating user social network information and message semantic relationship. The contributions of this paper are three-fold: (1) We convert predicting user retweeting behavior problem to solve a probabilistic matrix factorization problem; (2) Following the intuition that user social network relationship will affect the retweeting behavior, we extensively study how to model social information to improve the prediction performance; and (3) We also incorporate message semantic embedding to constrain the objective function by making a full use of additional the messages’ content-based and structure-based features. The empirical results and analysis demonstrate that our method significantly outperform the state-of-the-art approaches.
Journal: Procedia Computer Science - Volume 80, 2016, Pages 356–365