Collaborative Filtering for Personalised Facet Selection

Abstract

An overwhelming number of facet values causes difficulties in providing an efficient search filter in dynamic facet search. It requires effort and time from the searchers to examine the list in order to select their interested facets. Personalised facet selection provides a list of relevant facet which is related to the user's interests. However, personalisation may not be possible to determine a user's current interest from the user's profile or the user's history search only. In some cases, due to insufficient information to identify users' current interests, the need of associating community opinions with personal interests is necessary. This study aims to investigate the incorporation of a collaborative approach to personalise facet selection. Collaborative Filtering is employed to address the issue of limited profile information and the approach has been widely used in recommender systems. Experiments were conducted on a benchmark Movie dataset using user ratings as the representation of user preferences and evaluated by rating prediction accuracy and computational time. The results show that Collaborative Filtering should improve the performance of personalised facet selection.