Name
Abstract | ||
|---|---|---|
Consumer reviews play an important role in various e-commerce sites like hotel reservation and app stores. Online consumer reviews are informative because they convey consumers' actual experiences and evaluations to the products and services they received. In this paper, we leverage the consumer reviews to develop a review-driven recommender service for e-commerce websites. We semantically explore the topics in reviews to derive the product features and infer the preferences of consumers for making recommendations. We conduct large-scale experiments on real-world data. The results manifest that the proposed recommender service is effective for web e-commerce. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/SOCA.2017.35 | 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA) |
Keywords | Field | DocType |
recommender systems,e-commerce,topic modeling | Reservation,Recommender system,World Wide Web,Leverage (finance),Computer science,Data pre-processing,E-commerce,Distributed computing | Conference |
ISSN | ISBN | Citations |
2163-2871 | 978-1-5386-1327-6 | 2 |
PageRank | References | Authors |
0.38 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keng-Pei Lin | 1 | 117 | 11.61 |
| Chih-Ya Shen | 2 | 103 | 17.13 |
| Tzu-Lin Chang | 3 | 2 | 0.38 |
| Te-Min Chang | 4 | 34 | 6.29 |