poster

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

Authors:

Romain Deveaud,

M-Dyaa Albakour,

Craig Macdonald,

Iadh OunisAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 1827 - 1830

https://doi.org/10.1145/2661829.2661956

Published: 03 November 2014 Publication History

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.

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Cited By

Zhou ZWeibel RHuang H(2021)Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approachInternational Journal of Geographical Information Science10.1080/13658816.2021.194654236:3(514-546)Online publication date: 19-Jul-2021
https://doi.org/10.1080/13658816.2021.1946542
Manotumruksa JMacdonald COunis ICollins-Thompson KMei QDavison BLiu YYilmaz E(2018)A Contextual Attention Recurrent Architecture for Context-Aware Venue RecommendationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210042(555-564)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210042
Arampatzis AKalamatianos G(2017)Suggesting Points-of-Interest via Content-Based, Collaborative, and Hybrid Fusion Methods in Mobile DevicesACM Transactions on Information Systems10.1145/312562036:3(1-28)Online publication date: 29-Sep-2017
https://dl.acm.org/doi/10.1145/3125620
Show More Cited By

Index Terms

On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014 ACM.

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Published: 03 November 2014

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Seventh Framework Programme

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CIKM '14

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CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

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CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhou ZWeibel RHuang H(2021)Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approachInternational Journal of Geographical Information Science10.1080/13658816.2021.194654236:3(514-546)Online publication date: 19-Jul-2021
https://doi.org/10.1080/13658816.2021.1946542
Manotumruksa JMacdonald COunis ICollins-Thompson KMei QDavison BLiu YYilmaz E(2018)A Contextual Attention Recurrent Architecture for Context-Aware Venue RecommendationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210042(555-564)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210042
Arampatzis AKalamatianos G(2017)Suggesting Points-of-Interest via Content-Based, Collaborative, and Hybrid Fusion Methods in Mobile DevicesACM Transactions on Information Systems10.1145/312562036:3(1-28)Online publication date: 29-Sep-2017
https://dl.acm.org/doi/10.1145/3125620
Samar TBellogín AVries A(2016)The strange case of reproducibility versus representativeness in contextual suggestion test collectionsInformation Retrieval10.1007/s10791-015-9276-919:3(230-255)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s10791-015-9276-9
Manotumruksa JMacdonald COunis I(2016)Predicting Contextually Appropriate Venues in Location-Based Social NetworksExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-319-44564-9_8(96-109)Online publication date: 23-Aug-2016
https://doi.org/10.1007/978-3-319-44564-9_8
Manotumruksa JKompatsiaris IPapadopoulos STsikrika TVrochidis S(2015)Users location prediction in location-based social networksProceedings of the 6th Symposium on Future Directions in Information Access10.14236/ewic/FDIA2015.11(44-47)Online publication date: 2-Sep-2015
https://dl.acm.org/doi/10.14236/ewic/FDIA2015.11
Deveaud RAlbakour MMacdonald COunis IBailey JMoffat AAggarwal Cde Rijke MKumar RMurdock VSellis TYu J(2015)Experiments with a Venue-Centric Model for Personalisedand Time-Aware Venue SuggestionProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806484(53-62)Online publication date: 17-Oct-2015
https://dl.acm.org/doi/10.1145/2806416.2806484

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