Name
Abstract | ||
|---|---|---|
Product reviews greatly influence purchase decisions in online shopping. A common burden of online shopping is that consumers have to search for the right answers through massive reviews, especially on popular products. Hence, estimating and predicting the helpfulness of reviews become important tasks to directly improve shopping experience. In this paper, we propose a new approach to helpfulness prediction by leveraging aspect analysis of reviews. Our hypothesis is that a helpful review will cover many aspects of a product at different emphasis levels. The first step to tackle this problem is to extract proper aspects. Because related products share common aspects to different degrees, we propose an aspect extraction model making use of product category information to balance the aspects of a general category and those of subcategories under it. On top of this model, a two-layer regressor is trained for helpfulness prediction. Experiment results show that we can improve helpfulness prediction by 7% than the baseline on 5 popular product categories from Amazon.com. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICTAI.2016.127 | 2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016) |
Field | DocType | ISSN |
Data science,Helpfulness,Computer science,Feature extraction,Artificial intelligence,Product reviews,Product category,Product (category theory),Semantics,Machine learning | Conference | 1082-3409 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinfei Yang | 1 | 99 | 16.53 |
| Chen Cen | 2 | 162 | 25.61 |
| Sheng Bao | 3 | 215 | 26.77 |