@inproceedings{5b84a4c47a7b480d9cd00e27e0a904c8,
title = "Multi-task neural learning architecture for end-to-end identification of helpful reviews",
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.",
keywords = "Attention mechanism, Deep neural networks, E-commerce, Helpful review identification, Multi-task learning",
author = "Miao Fan and Yue Feng and Mingming Sun and Ping Li and Haifeng Wang and Jianmin Wang",
note = "Funding Information: This work was in part supported by the Joint Post-Doctoral Program of Baidu Inc. and Tsinghua University. The authors are grateful to the anonymous reviewers for their valuable and constructive comments. Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
month = oct,
day = "24",
doi = "10.1109/ASONAM.2018.8508623",
language = "English (US)",
series = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "343--350",
editor = "Andrea Tagarelli and Chandan Reddy and Ulrik Brandes",
booktitle = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",
address = "United States",
}