A comparison of general vs personalised affective models for the prediction of topical relevance

Arapakis, I., Athanasakos, K. and Jose, J.M. (2010) A comparison of general vs personalised affective models for the prediction of topical relevance. In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland, 19-23 Jul 2010, pp. 371-378. (doi: 10.1145/1835449.1835512)

Abstract

Information retrieval systems face a number of challenges, originating mainly from the semantic gap problem. Implicit feedback techniques have been employed in the past to address many of these issues. Although this was a step towards the right direction, a need to personalise and tailor the search experience to the user-specific needs has become evident. In this study we examine ways of personalising affective models trained on facial expression data. Using personalised data we adapt these models to individual users and compare their performance to a general model. The main goal is to determine whether the behavioural differences of users have an impact on the models' ability to determine topical relevance and if, by personalising them, we can improve their accuracy. For modelling relevance we extract a set of features from the facial expression data and classify them using Support Vector Machines. Our initial evaluation indicates that accounting for individual differences and applying personalisation introduces, in most cases, a noticeable improvement in the models' performance.

• Item Type: Conference Proceedings
• Additional Information: ISBN: 9781450301534
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Arapakis, Mr Ioannis
• Authors: Arapakis, I., Athanasakos, K., and Jose, J.M.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science