Name
Abstract | ||
|---|---|---|
The amount of information in e-Commerce is increasing far more quickly than our ability to process. Recommender systems apply knowledge discovery techniques to help people find what they really want. However, all of the previous approaches have an important drawback: items added newly cannot be found. In this paper, a general framework is proposed for supporting automatic recommendation of the new item to the potential users based on the concept of influent sets. We propose a simple efficient indexing structure and a heuristic information retrieval technique algorithm for searching reverse k nearest neighbour in high-dimensional dataset. And experimental evaluation reveals that our approach outperforms the previous algorithm and enhances the performance efficiently. © 2005 IEEE. |
Year | DOI | Venue |
|---|---|---|
2005 | null | Proceedings - 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE-05 |
Keywords | DocType | Volume |
knowledge discovery,e commerce,recommender system,information retrieval | Conference | null |
Issue | ISSN | ISBN |
null | null | 0-7695-2274-2 |
Citations | PageRank | References |
4 | 0.42 | 6 |
Authors | ||
3 | ||