Abstract
We present an algorithm for automatically ranking user-generated book reviews according to review helpfulness. Given a collection of reviews, our REVRANK algorithm identifies a lexicon of dominant terms that constitutes the core of a virtual optimal review. This lexicon defines a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a 'virtual core' review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. Our experiments show that REVRANK clearly outperforms a baseline imitating the Amazon user vote review ranking system.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 |
| Publisher | AAAI Press |
| Pages | 154-161 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781577354215 |
| DOIs | |
| State | Published - 20 May 2009 |
| Event | 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 - San Jose, United States Duration: 17 May 2009 → 20 May 2009 |
Publication series
| Name | Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 |
|---|
Conference
| Conference | 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 |
|---|---|
| Country/Territory | United States |
| City | San Jose |
| Period | 17/05/09 → 20/05/09 |
Bibliographical note
Publisher Copyright:Copyright © 2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.