Name
Abstract | ||
|---|---|---|
Consumer reviews, opinions, and shared experiences in using a product are a powerful source of information that recommender systems can use. Despite the importance and value of such information, no comprehensive mechanism formalizes the opinions' selection, retrieval, and use owing to the difficulty of extracting information from text data. A new recommender system prioritizes consumer product reviews on the basis of the reviewer's level of expertise in using a product. The system uses text mining techniques to map each piece of each review comment into an ontology. Using consumer reviews also helps solve the cold-start problem that plagues traditional approaches. This article is part of a special issue on Recommender Systems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/MIS.2007.55 | IEEE Intelligent Systems |
Keywords | Field | DocType |
comprehensive mechanism,recommender system,consumer review,informed recommender,basing recommendations,consumer product reviews,consumer product review,new recommender system prioritizes,cold-start problem,text mining technique,recommender systems,text data,powerful source,consumer goods,data mining,collaborative filtering,ontology,electronic commerce,text mining,consumer products,text analysis | Recommender system,Ontology,Data mining,World Wide Web,Collaborative filtering,Information retrieval,Computer science,Product reviews | Journal |
Volume | Issue | ISSN |
22 | 3 | 1541-1672 |
Citations | PageRank | References |
76 | 2.83 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvana Aciar | 1 | 107 | 8.02 |
| Debbie Zhang | 2 | 129 | 8.05 |
| Simeon Simoff | 3 | 542 | 72.16 |
| John Debenham | 4 | 238 | 17.03 |