کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
484099 703253 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
RTPMF: Leveraging User and Message Embeddings for Retweeting Behavior Prediction
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
RTPMF: Leveraging User and Message Embeddings for Retweeting Behavior Prediction
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 80, 2016, Pages 356–365
نویسندگان
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