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

摘要

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.