Paper Info
Title
RankFeed - Recommendation as Searching without Queries: New Hybrid Method of Recommendation
Abstract
The paper describes RankFeed a new adaptive method of recommendation that benefits from similarities between searching and recommendation. Concepts such as: the initial ranking, the positive and negative feedback widely used in searching are applied to recommendation in order to enhance its coverage, maintaining high accuracy. There are four principal factors that determine the method's behaviour: the quality document ranking, navigation patterns, textual similarity and the list of recommended pages that have been ignored during the navigation. In the evaluation part, the local site's behaviour of the RankFeed ranking is contrasted with PageRank. Additionally, recommendation behaviour of RankFeed versus other classical approaches is evaluated.
Year
Venue
Keywords
2005
JOURNAL OF UNIVERSAL COMPUTER SCIENCE
recommendation,information retrieval,Web mining,personalization,adaptive systems,ROSA
Field
DocType
Volume
Data mining,PageRank,Information retrieval,Ranking,Adaptive method,Computer science
Journal
11
Issue
ISSN
Citations 
2
0948-695X
1
PageRank 
References 
Authors
0.36
20
1
Name
Order
Citations
PageRank
Maciej Kiewra1466.14