Multi-task neural learning architecture for end-to-end identification of helpful reviews

Miao Fan, Yue Feng, Mingming Sun, Ping Li, Haifeng Wang, Jianmin Wang

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

17 Scopus citations

Abstract

Helpful reviews play a pivotal role in recommending desirable goods and accelerating purchase decisions of customers in e-commercial services. Given a large proportion of product reviews with unknown helpfulness/unhelpfulness, the research on automatic identification of helpful reviews has drawn much attention in recent years. However, state-of-the-art approaches still rely heavily on extracting heuristic text features from reviews with domain-specific knowledge. In this paper, we first introduce a multi-task neural learning (MTNL) architecture for identifying helpful reviews. The end-to-end neural architecture can learn to reconstruct effective features upon the raw input of words and even characters, and the multi-task learning paradigm helps to make more accurate predictions of helpful reviews based on a secondary task which fits the star ratings of reviews. We also build two datasets containing helpful/unhelpful reviews from different product categories in Amazon, and compare the performance of MTNL with several mainstream methods on both datasets. Experimental results confirm that MTNL outperforms the state-of-the-art approaches by a significant margin.

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
EditorsAndrea Tagarelli, Chandan Reddy, Ulrik Brandes
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages343-350
Number of pages8
ISBN (Electronic)9781538660515
DOIs
StatePublished - Oct 24 2018
Externally publishedYes
Event10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain
Duration: Aug 28 2018Aug 31 2018

Publication series

NameProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Other

Other10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Country/TerritorySpain
CityBarcelona
Period8/28/188/31/18

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Communication
  • Computer Networks and Communications
  • Information Systems and Management

Keywords

  • Attention mechanism
  • Deep neural networks
  • E-commerce
  • Helpful review identification
  • Multi-task learning

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