This purpose of this paper is to propose a recommendation approach for information retrieval.
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.
Experimental results have shown the effectiveness of the approach.
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.
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/10.1108/07378830510636364Download as .RIS
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