ARTICLE KNOWLEDGE GRAPH

Analysis of interlinked descriptions of entities - objects, events, situations or abstract concepts – while also encoding the semantics





Id 695
Author Bermingham A., ORourke J., Gurrin C., Collins R., Irving K., Smeaton A.F.
Title Automatically recommending multimedia content for use in group reminiscence therap
Reference

Bermingham A., ORourke J., Gurrin C., Collins R., Irving K., Smeaton A.F.; Automatically recommending multimedia content for use in group reminiscence therap ;MIIRH 2013 - Proceedings of the 1st ACM International Workshop on Multimedia Indexing and Information Retrieval for Heathcare, Co-located with ACM Multimedia 2013 vol: issue: page:49.0

Keywords healthcare; information retrieval; multimedia; recommender systems; reminiscence therapy; search; usability
Link to article https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887189046&doi=10.1145%2f2505323.2505333&partnerID=40&md5=82730de1f23525bb5a7f5a684ed97a3e
Abstract This paper presents and evaluates a novel approach for automatically recommending multimedia content for use in group reminiscence therapy for people with Alzheimers and other dementias. In recent years recommender systems have seen popularity in providing a personalised experience in information discovery tasks. This personalisation approach is naturally suited to tasks in healthcare, such as reminiscence therapy, where there has been a trend towards an increased emphasis on person-centred care. Building on recent work which has shown benefits to reminiscence therapy in a group setting, we develop and evaluate a system, REMPAD, which profiles people with Alzheimers and other dementias, and provides multimedia content tailored to a given group context. In this paper we present our system and approach, and report on a user trial in residential care settings. In our evaluation we examine the potential to use early-aggregation and late-aggregation of group member preferences using case-based reasoning combined with a content-based method. We evaluate with respect to accuracy, utility and perceived usefulness. The results overall are positive and we find that our best-performing approach uses early aggregation CBR combined with a content-based method. Also, under different evaluation criteria, we note different performances, with certain configurations of our approach providing better accuracy and others providing better utility. © 2013 ACM.

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