Paper Info
Title
Multi-Domain Gated CNN for Review Helpfulness Prediction
Abstract
Consumers today face too many reviews to read when shopping online. Presenting the most helpful reviews, instead of all, to them will greatly ease purchase decision making. Most of the existing studies on review helpfulness prediction focused on domains with rich labels, not suitable for domains with insufficient labels. In response, we explore a multi-domain approach that learns domain relationships to help the task by transferring knowledge from data-rich domains to data-deficient domains. To better model domain differences, our approach gates multi-granularity embeddings in a Neural Network (NN) based transfer learning framework to reflect the domain-variant importance of words. Extensive experiments empirically demonstrate that our model outperforms the state-of-the-art baselines and NN-based methods without gating on this task. Our approach facilitates more effective knowledge transfer between domains, especially when the target domain dataset is small. Meanwhile, the domain relationship and domain-specific embedding gating are insightful and interpretable.
Year
DOI
Venue
2019
10.1145/3308558.3313587
WWW '19: The Web Conference on The World Wide Web Conference WWW 2019
Keywords
Field
DocType
Review helpfulness prediction, transfer learning
Embedding,Helpfulness,Computer science,Transfer of learning,Knowledge transfer,Multi domain,Artificial intelligence,Artificial neural network,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6674-8
2
0.38
References 
Authors
0
7
Name
Order
Citations
PageRank
Chen Cen116225.61
Minghui Qiu259334.84
Yinfei Yang39916.53
Jun Zhou4102.89
Jun Huang57711.67
Xiaolong Li636236.92
Sheng Bao721526.77