Paper Info
Title
On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions
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.
Year
DOI
Venue
2014
10.1145/2661829.2661956
CIKM
Keywords
Field
DocType
venue recommendation,contextual suggestion,personalisation,information search and retrieval,learning to rank
Data mining,Learning to rank,Information retrieval,Computer science,Language modelling,Personalization
Conference
Citations 
PageRank 
References 
11
0.60
14
Authors
4
Name
Order
Citations
PageRank
Romain Deveaud1110.60
M-Dyaa Albakour216314.15
Craig Macdonald32588178.50
Iadh Ounis43438234.59