Context modelling for situation-sensitive recommendations

Whiting, S. and Jose, J. (2011) Context modelling for situation-sensitive recommendations. Lecture Notes in Computer Science, 7022, pp. 376-387. (doi: 10.1007/978-3-642-24764-4_33)

Abstract

Users are finding themselves interacting with increasingly complex software systems and expanding information resources. However many of these systems have little to no awareness of the personally-understood user context which expresses why they are being used. In this paper we propose a framework for modelling and proactively retrieving previously accessed and created information objects and resources that are within the context of a user’s current situation. We first consider theories of context to understand the discrete aspects of context that may delineate a user’s composite situations. With this we develop a framework for modelling user interaction in context along with a re-configurable algorithm for making personal recommendations for desired information objects based upon the environmental, content-based and task sequence contextual similarity of the current situation to past situations. To measure the effectiveness of our approach we use a two week activity log from four real users in a preliminary lab-based evaluation methodology. Initial results suggest the framework as a static personal recommendation algorithm is effective to varying degrees during periods of interaction for users of various characteristics.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon
• Authors: Whiting, S., and Jose, J.
• College/School: College of Science and Engineering > School of Chemistry
• Journal Name: Lecture Notes in Computer Science
• Publisher: Springer
• ISSN: 0302-9743