NDLI: Who should share what?: item-level social influence prediction for users and posts ranking

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Who should share what?: item-level social influence prediction for users and posts ranking

Content Provider	ACM Digital Library
Author	Cui, Peng Ou, Mingdong Liu, Shaowei Yang, Shiqiang Wang, Fei Sun, Lifeng
Abstract	People and information are two core dimensions in a social network. People sharing information (such as blogs, news, albums, etc.) is the basic behavior. In this paper, we focus on predicting item-level social influence to answer the question Who should share What, which can be extended into two information retrieval scenarios: (1) Users ranking: given an item, who should share it so that its diffusion range can be maximized in a social network; (2) Web posts ranking: given a user, what should she share to maximize her influence among her friends. We formulate the social influence prediction problem as the estimation of a user-post matrix, in which each entry represents the strength of influence of a user given a web post. We propose a Hybrid Factor Non-Negative Matrix Factorization (HF-NMF) approach for item-level social influence modeling, and devise an efficient projected gradient method to solve the HF-NMF problem. Intensive experiments are conducted and demonstrate the advantages and characteristics of the proposed method.
Starting Page	185
Ending Page	194
Page Count	10
File Format	PDF
ISBN	9781450307574
DOI	10.1145/2009916.2009945
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social influence Matrix factorization User ranking Web post ranking
Content Type	Text
Resource Type	Article

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