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An exploration of user–facet interaction in collaborative-based personalized multiple facet selection

Chantamunee, S., Wong, K.W. and Fung, C.C.ORCID: 0000-0001-5182-3558 (2020) An exploration of user–facet interaction in collaborative-based personalized multiple facet selection. Knowledge-Based Systems, 209 . Art. 106444.

Link to Published Version: https://doi.org/10.1016/j.knosys.2020.106444
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Abstract

The huge amount of irrelevant and unimportant information have led to the need of using personalization in selecting the information which is relevant to searchers’ interest. Personalized faceted search has been a potential tool to support searchers to retrieve appropriate information effectively by navigating a list of selected multiple facets or categories based on the search results. To develop an effective personalized faceted search, the selection of relevant multiple facets is an important mechanism. Collaborative-based personalization was introduced for facet selection. Recently, Artificial Neural Network (ANN) has been reported that it performs better than other state-of-the-art Collaborative Filtering techniques for predicting single facet. However, analyzing the collaborative interests for multiple facets has not been studied. It is challenging if the interaction of the users on multiple facets is based on the information associated with the preferences of similar users over a group of multiple facets. This paper proposes an ANN-based facet predictive model that makes use of the collaborative-based personalization concept for multiple facet selection. The architecture of the proposed model is based on two suitable interaction schemes, the Early interaction and the Late interaction schemes. Based on experimental results, the performance was evaluated in terms of prediction accuracy and computation time. The results showed that the proposed model based on an effective interaction scheme obtained significant improvement on the prediction of personal interests on multiple facets.

Item Type: Journal Article
Murdoch Affiliation: Information Technology, Mathematics and Statistics
Publisher: Elsevier BV
Copyright: © 2020 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/57989
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