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Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation

Chakraborty, Anirban orcid logoORCID: 0000-0001-7425-6664, Ganguly, Debasis orcid logoORCID: 0000-0003-0050-7138, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545 and Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 (2022) Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation. Information Retrieval Journal . ISSN 1573-7659

An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized `points-of-interest' (POIs) to a user, if it can extract information from the user's preference history (exploitation) and effectively blend it with the user's current contextual information (exploration) to predict a POI's `appropriateness' in the current context. To balance this trade-off between exploitation and exploration, we propose an unsupervised, generic framework involving a factored relevance model (FRLM), constituting two distinct components, one pertaining to historical contexts, and the other corresponding to the current context. We further generalize the proposed FRLM by incorporating the semantic relationships between terms in POI descriptors using kernel density estimation (KDE) on embedded word vectors. Additionally, we show that trip-qualifiers, (e.g. `trip-type', `accompanied-by') are potentially useful information sources that could be used to improve the recommendation effectiveness. Using such information is not straightforward since users' texts/reviews of visited POIs typically do not explicitly contain such annotations. We undertake a weakly supervised approach to predict the associations between the review-texts in a user profile and the likely trip contexts. Our experiments, conducted on the TREC Contextual Suggestion 2016 dataset, demonstrate that factorization, KDE-based generalizations, and trip-qualifier enriched contexts of the relevance model improve POI recommendation.
Item Type:Article (Published)
Uncontrolled Keywords:Relevance Model; Contextual Recommendation; User Preference Model; Word-Tag Semantics; Word Embedding; Kernel Density Estimation
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Official URL:https://doi.org/10.1007/s10791-021-09400-9
Copyright Information:© 2022 Springer
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 (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund
ID Code:26639
Deposited On:01 Apr 2022 08:39 by Annalina Caputo . Last Modified 21 Jan 2023 04:30

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