Name
Abstract | ||
|---|---|---|
Recommender systems have become, like search engines, a tool that cannot be ignored by a website with a large selection of products, music, news or simply webpages. The performance of this kind of systems depends on a large amount of information. Meanwhile, the amount of information available in the Web is continuously growing. In this paper, we propose to provide recommendation from unstructured textual data. The method has two steps. First, subjective texts are labelled according to their expressed opinion. Second, the results are used to provide recommendations thanks to a collaborative filtering technique. We describe the complete processing chain and evaluate it.
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Year | Venue | Keywords |
|---|---|---|
2010 | Recherche d'Information Assistee par Ordinateur | recommender system,unstructured textual data,towards text-based recommendation,large amount,subjective text,search engine,large selection,complete processing chain,collaborative filtering,recommender systems,user generated content |
Field | DocType | ISBN |
Recommender system,User-generated content,World Wide Web,Search engine,Collaborative filtering,Information retrieval,Web page,Computer science | Conference | 978-2-905450-09-8 |
Citations | PageRank | References |
4 | 0.43 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Damien Poirier | 1 | 14 | 1.74 |
| isabelle tellier | 2 | 84 | 20.31 |
| Françoise Fessant | 3 | 115 | 10.08 |
| Julien Schluth | 4 | 5 | 0.77 |