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A Factored Relevance Model for Contextual Point-of-Interest Recommendation

Chakraborty, Anirban ORCID: 0000-0001-7425-6664, Ganguly, Debasis ORCID: 0000-0003-0050-7138, Caputo, Annalina ORCID: 0000-0002-7144-8545 and Lawless, Seamus ORCID: 0000-0001-6302-258X (2019) A Factored Relevance Model for Contextual Point-of-Interest Recommendation. In: ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '19), 2–5 Oct 2019, Santa Clara, CA, USA. ISBN 978-1-4503-6881-0

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Abstract

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Relevance Model; Contextual Recommendation; User Model
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval. ICTIR '19 . Association for Computing Machinery (ACM). ISBN 978-1-4503-6881-0
Publisher:Association for Computing Machinery (ACM)
Official URL:https://dx.doi.org/10.1145/3341981.3344230
Copyright Information:© 2019 Association for Computing Machinery
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland Research Centres Programme(Grant13/RC/2106)and the European Regional Development Fund, European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No.:71356
ID Code:26487
Deposited On:26 Nov 2021 11:33 by Annalina Caputo . Last Modified 26 Nov 2021 11:54

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