Name
Abstract | ||
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Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising research on building automatic mechanisms to assess review helpfulness. The mainstream methods generally extract various linguistic and embedding features solely from the text of a review as the evidence for helpfulness prediction. We, however, consider that the helpfulness of a review should be fully aware of the metadata (such as the title, the brand, the category, and the description) of its target product, besides the textual content of the review itself. Hence, in this paper we propose an end-to-end deep neural architecture directly fed by both the metadata of a product and the raw text of its reviews to acquire product-aware review representations for helpfulness prediction. The learned representations do not require tedious labor on feature engineering and are expected to be more informative as the target-aware evidence to assess the helpfulness of online reviews. We also construct two large-scale datasets which are a portion of the real-world web data in Amazon and Yelp, respectively, to train and test our approach. Experiments are conducted on two different tasks: helpfulness identification and regression of online reviews, and results demonstrate that our approach can achieve state-of-the-art performance with substantial improvements.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308558.3313523 | WWW '19: The Web Conference on The World Wide Web Conference WWW 2019 |
Keywords | Field | DocType |
E-commerce, benchmark datasets, helpfulness prediction, neural networks, online reviews | Metadata,World Wide Web,Architecture,Helpfulness,Information retrieval,Computer science,Feature engineering,Artificial neural network,E-commerce | Conference |
ISBN | Citations | PageRank |
978-1-4503-6674-8 | 3 | 0.41 |
References | Authors | |
0 | 5 | |