On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions

Deveaud, R., Albakour, M.-D., Macdonald, C. and Ounis, I. (2014) On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions. In: CIKM '14: 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China, 3-7 Nov 2014, pp. 1827-1830. ISBN 9781450325981 (doi:10.1145/2661829.2661956)

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Abstract

Suggesting venues to a user in a given geographic context is an emerging task that is currently attracting a lot of attention. Existing studies in the literature consist of approaches that rank candidate venues based on different features of the venues and the user, which either focus on modeling the preferences of the user or the quality of the venue. However, while providing insightful results and conclusions, none of these studies have explored the relative effectiveness of these different features. In this paper, we explore a variety of user-dependent and venue-dependent features and apply state-of-the-art learning to rank approaches to the problem of contextual suggestion in order to find what makes a venue relevant for a given context. Using the test collection of the TREC 2013 Contextual Suggestion track, we perform a number of experiments to evaluate our approach. Our results suggest that a learning to rank technique can significantly outperform a Language Modelling baseline that models the positive and negative preferences of the user. Moreover, despite the fact that the contextual suggestion task is a personalisation task (i.e. providing the user with personalised suggestions of venues), we surprisingly find that user-dependent features are less effective than venue-dependent features for estimating the relevance of a suggestion.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Deveaud, Mr Romain and Albakour, Dr M-Dyaa
Authors: Deveaud, R., Albakour, M.-D., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450325981
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