TY  - JOUR
AB  - 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.
VL  - 23
IS  - 4
SN  - 0737-8831
DO  - 10.1108/07378830510636364
UR  - https://doi.org/10.1108/07378830510636364
AU  - Gao Kai
AU  - Wang Yong‐Cheng
AU  - Wang Zhi‐Qi
ED  - Scott P. Muir
ED  - Mark Leggott
PY  - 2005
Y1  - 2005/01/01
TI  - Similar interest clustering and partial back‐propagation‐based recommendation in digital library
T2  - Library Hi Tech
PB  - Emerald Group Publishing Limited
SP  - 587
EP  - 597
Y2  - 2024/04/25
ER  -