Similar interest clustering and partial back‐propagation‐based recommendation in digital library

Kai Gao (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)
Yong‐Cheng Wang (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)
Zhi‐Qi Wang (Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China)

Library Hi Tech

ISSN: 0737-8831

Publication date: 1 December 2005

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

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2005, Emerald Group Publishing Limited

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