Will I win your favor? Predicting the success of altruistic requests

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

As those in need increasingly ask for favors in online social services, having a technique to accurately predict whether their requests will be successful can instantaneously help them better formulating the requests. This paper aims to boost the accuracy of predicting the success of altruistic requests, by following the similar setting of the state-of-theart work ADJ [1]. While ADJ has an unsatisfying prediction accuracy and requires a large set of training data, we develop a novel request success prediction model, termed Graph-based Predictor for Request Success (GPRS). Our GPRS model is featured by learning the correlation between success or not and the set of features extracted in the request, together with a label propagation-based optimization mechanism. Besides, in addition to the textual, social, and temporal features proposed by ADJ, we further propose three effective features, including centrality, role, and topic features, to capture how users interact in the history and how different topics affect the success of requests. Experiments conducted on the requests in the “Random Acts of Pizza” community of Reddit.com show GPRS can lead to around 0.81 and 0.68 AUC scores using sufficient and limited training data respectively, which significantly outperform ADJ by 0.14 and 0.08 respectively.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
編輯Ruili Wang, James Bailey, Takashi Washio, Joshua Zhexue Huang, Latifur Khan, Gillian Dobbie
發行者Springer Verlag
頁面177-188
頁數12
ISBN(列印)9783319317526
DOIs
出版狀態Published - 2016
事件20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand
持續時間: 2016 四月 192016 四月 22

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9651
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
國家/地區New Zealand
城市Auckland
期間16-04-1916-04-22

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 電腦科學(全部)

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