Paper Info
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.
Year
DOI
Venue
2018
10.5555/3382225.3382298
ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining Barcelona Spain August, 2018
Keywords
Field
DocType
Helpful review identification, E-commerce, Multi-task learning, Deep neural networks, Attention mechanism
Neural learning,Architecture,Heuristic,Helpfulness,Multi-task learning,Computer science,End-to-end principle,Artificial intelligence,Product (category theory),Machine learning,E-commerce
Conference
ISSN
ISBN
Citations 
2473-9928
978-1-5386-6051-5
3
PageRank 
References 
Authors
0.41
11
6
Name
Order
Citations
PageRank
Miao Fan114016.04
Yue Feng25516.15
Mingming Sun3246.27
Ping Li41672127.72
Haifeng Wang580694.25
Jianmin Wang62446156.05