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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
EditorsRuili Wang, James Bailey, Takashi Washio, Joshua Zhexue Huang, Latifur Khan, Gillian Dobbie
PublisherSpringer Verlag
Pages177-188
Number of pages12
ISBN (Print)9783319317526
DOIs
Publication statusPublished - 2016
Event20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand
Duration: 2016 Apr 192016 Apr 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9651
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
CountryNew Zealand
CityAuckland
Period16-04-1916-04-22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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