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 Jan 1
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

Fingerprint

Predictors
Graph in graph theory
Labels
Centrality
Large Set
Prediction Model
Propagation
Sufficient
Predict
Optimization
Prediction
Experiments
Experiment
Training
Model
Learning
History
Community

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hsieh, H-P., Yan, R., & Li, C-T. (2016). Will I win your favor? Predicting the success of altruistic requests. In R. Wang, J. Bailey, T. Washio, J. Z. Huang, L. Khan, & G. Dobbie (Eds.), Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings (pp. 177-188). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9651). Springer Verlag. https://doi.org/10.1007/978-3-319-31753-3_15
Hsieh, Hsun-Ping ; Yan, Rui ; Li, Cheng-Te. / Will I win your favor? Predicting the success of altruistic requests. Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. editor / Ruili Wang ; James Bailey ; Takashi Washio ; Joshua Zhexue Huang ; Latifur Khan ; Gillian Dobbie. Springer Verlag, 2016. pp. 177-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{5dee44f5e5bb4186bdc51d3c56cee7c9,
title = "Will I win your favor? Predicting the success of altruistic requests",
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.",
author = "Hsun-Ping Hsieh and Rui Yan and Cheng-Te Li",
year = "2016",
month = "1",
day = "1",
doi = "10.1007/978-3-319-31753-3_15",
language = "English",
isbn = "9783319317526",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "177--188",
editor = "Ruili Wang and James Bailey and Takashi Washio and Huang, {Joshua Zhexue} and Latifur Khan and Gillian Dobbie",
booktitle = "Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings",
address = "Germany",

}

Hsieh, H-P, Yan, R & Li, C-T 2016, Will I win your favor? Predicting the success of altruistic requests. in R Wang, J Bailey, T Washio, JZ Huang, L Khan & G Dobbie (eds), Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9651, Springer Verlag, pp. 177-188, 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, Auckland, New Zealand, 16-04-19. https://doi.org/10.1007/978-3-319-31753-3_15

Will I win your favor? Predicting the success of altruistic requests. / Hsieh, Hsun-Ping; Yan, Rui; Li, Cheng-Te.

Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. ed. / Ruili Wang; James Bailey; Takashi Washio; Joshua Zhexue Huang; Latifur Khan; Gillian Dobbie. Springer Verlag, 2016. p. 177-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9651).

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

TY - GEN

T1 - Will I win your favor? Predicting the success of altruistic requests

AU - Hsieh, Hsun-Ping

AU - Yan, Rui

AU - Li, Cheng-Te

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84964054560&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964054560&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-31753-3_15

DO - 10.1007/978-3-319-31753-3_15

M3 - Conference contribution

AN - SCOPUS:84964054560

SN - 9783319317526

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 177

EP - 188

BT - Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings

A2 - Wang, Ruili

A2 - Bailey, James

A2 - Washio, Takashi

A2 - Huang, Joshua Zhexue

A2 - Khan, Latifur

A2 - Dobbie, Gillian

PB - Springer Verlag

ER -

Hsieh H-P, Yan R, Li C-T. Will I win your favor? Predicting the success of altruistic requests. In Wang R, Bailey J, Washio T, Huang JZ, Khan L, Dobbie G, editors, Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. Springer Verlag. 2016. p. 177-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-31753-3_15