A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
β Scribed by Huang, Zan ;Zeng, Daniel ;Chen, Hsinchun
- Book ID
- 121371730
- Publisher
- IEEE
- Year
- 2007
- Tongue
- English
- Weight
- 374 KB
- Volume
- 22
- Category
- Article
- ISSN
- 1541-1672
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π SIMILAR VOLUMES
The information overload on the World Wide Web results in the underuse of some existing egovernment services within the business domain. Small-to-medium businesses (SMBs), in particular, are seeking "one-to-one" e-services from government in current highly competitive markets, and there is an impera
Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlatio