Similar interest clustering and partial back‐propagation‐based recommendation in digital library
Abstract
Purpose
This purpose of this paper is to propose a recommendation approach for information retrieval.
Design/methodology/approach
Relevant results are presented on the basis of a novel data structure named FPT‐tree, which is used to get common interests. Then, data is trained by using a partial back‐propagation neural network. The learning is guided by users' click behaviors.
Findings
Experimental results have shown the effectiveness of the approach.
Originality/value
The approach attempts to integrate metric of interests (e.g., click behavior, ranking) into the strategy of the recommendation system. Relevant results are first presented on the basis of a novel data structure named FPT‐tree, and then, those results are trained through a partial back‐propagation neural network. The learning is guided by users' click behaviors.
Keywords
Citation
Gao, K., Wang, Y. and Wang, Z. (2005), "Similar interest clustering and partial back‐propagation‐based recommendation in digital library", Library Hi Tech, Vol. 23 No. 4, pp. 587-597. https://doi.org/10.1108/07378830510636364
Publisher
:Emerald Group Publishing Limited
Copyright © 2005, Emerald Group Publishing Limited