Deep autoencoder on personalized facet selection

Chantamunee, S., Wong, K.W. and Fung, C.C.ORCID: 0000-0001-5182-3558 (2019) Deep autoencoder on personalized facet selection. In: Gedeon, T., Wong, K.W. and Lee, M., (eds.) Proceedings. Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part IV. Springer, pp. 314-322.

Abstract

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep Autoencoder on the prediction of facet interests. Autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed Autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.

Item Type:	Book Chapter
Murdoch Affiliation(s):	Information Technology, Mathematics and Statistics
Publisher:	Springer
Other Information:	Part of the Communications in Computer and Information Science book series (CCIS, volume 1142)
URI:	http://researchrepository.murdoch.edu.au/id/eprint/57474

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