Integrating Collaborate and Content-Based Filtering for Personalized Information Recommendation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2005

AUTHORS

Zhiyun Xin , Jizhong Zhao , Ming Gu , Jiaguang Sun

ABSTRACT

To achieve high quality of push-based information service, in this paper, collaborative filtering and content-based adaptability approaches are surveyed for user-centered personalized information, then based on the above method, we proposed a mixed two-phased recommendation algorithm for high-quality information recommendation, upon which performance evaluations showed that the mixed algorithm is more efficient than pure content-based or collaborative filtering methods, for pure of either approaches is not so efficient for the lack of enough information need information. And moreover we found with large amount registered users, it is necessary and important for the system to serve users in a group mode, which involved merged retrieval issues. More... »

PAGES

476-482

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11596448_70

DOI

http://dx.doi.org/10.1007/11596448_70

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017532413


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