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A Cross-Platform Collection for Contextual Suggestion

Published: 07 August 2017 Publication History

Abstract

Suggesting personalized venues helps users to find interesting places on location-based social networks (LBSNs). Although there are many LBSNs online, none of them is known to have thorough information about all venues. The Contextual Suggestion track at TREC aimed at providing a collection consisting of places as well as user context to enable researchers to examine and compare different approaches, under the same evaluation setting. However, the officially released collection of the track did not meet many participants' needs related to venue content, online reviews, and user context. That is why almost all successful systems chose to crawl information from different LBSNs. For example, one of the best proposed systems in the TREC 2016 Contextual Suggestion track crawled data from multiple LBSNs and enriched it with venue-context appropriateness ratings, collected using a crowdsourcing platform. Such collection enabled the system to better predict a venue's appropriateness to a given user's context. In this paper, we release both collections that were used by the system above. We believe that these datasets give other researchers the opportunity to compare their approaches with the top systems in the track. Also, it provides the opportunity to explore different methods to predicting contextually appropriate venues.

References

[1]
Mohammad Aliannejadi and Fabio Crestani 2017. Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion SIGIR 2017. ACM.
[2]
Mohammad Aliannejadi, Ida Mele, and Fabio Crestani. 2016. User Model Enrichment for Venue Recommendation. In AIRS 2016. Springer, 212--223.
[3]
Li Chen, Guanliang Chen, and Feng Wang 2015. Recommender systems based on user reviews: the state of the art. UMUAI, Vol. 25, 2 (2015), 99--154.
[4]
Romain Deveaud, M-Dyaa Albakour, Craig Macdonald, and Iadh Ounis 2015. Experiments with a Venue-Centric Model for Personalised and Time-Aware Venue Suggestion CIKM 2015. ACM, 53--62.
[5]
Seyyed Hadi Hashemi, Charles L. A. Clarke, Jaap Kamps, Julia Kiseleva, and Ellen M. Voorhees. 2016. Overview of the TREC 2016 Contextual Suggestion Track TREC 2016. NIST.
[6]
Peilin Yang, Hongning Wang, Hui Fang, and Deng Cai. 2015. Opinions matter: a general approach to user profile modeling for contextual suggestion. Information Retrieval Journal Vol. 18, 6 (2015), 586--610.

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  • (2022)Point-of-Interest Recommendation System based on DeepWalk and Tensor Decomposition2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776076(867-872)Online publication date: 4-May-2022
  • (2022)Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendationInformation Retrieval10.1007/s10791-021-09400-925:1(44-90)Online publication date: 1-Mar-2022
  • (2022)Where Should I Go? A Deep Learning Approach to Personalize Type-Based Facet Ranking for POI SuggestionWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_17(207-215)Online publication date: 1-Jan-2022
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cover image ACM Conferences
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
August 2017
1476 pages
ISBN:9781450350228
DOI:10.1145/3077136
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 August 2017

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Author Tags

  1. collection
  2. context-awareness
  3. venue suggestion

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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  • (2022)Point-of-Interest Recommendation System based on DeepWalk and Tensor Decomposition2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776076(867-872)Online publication date: 4-May-2022
  • (2022)Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendationInformation Retrieval10.1007/s10791-021-09400-925:1(44-90)Online publication date: 1-Mar-2022
  • (2022)Where Should I Go? A Deep Learning Approach to Personalize Type-Based Facet Ranking for POI SuggestionWeb Information Systems Engineering – WISE 202110.1007/978-3-030-90888-1_17(207-215)Online publication date: 1-Jan-2022
  • (2021)Tag embedding based personalized point of interest recommendation systemInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10269058:6Online publication date: 1-Nov-2021
  • (2021)Dataset Creation Framework for Personalized Type-Based Facet Ranking Tasks EvaluationExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-030-85251-1_3(27-39)Online publication date: 14-Sep-2021
  • (2021)A Probabilistic Approach to Personalize Type-Based Facet Ranking for POI SuggestionWeb Engineering10.1007/978-3-030-74296-6_14(175-182)Online publication date: 18-May-2021
  • (2020)Relevance Models for Multi-Contextual Appropriateness in Point-of-Interest RecommendationProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401197(1981-1984)Online publication date: 25-Jul-2020
  • (2020)Retrievability based Document Selection for Relevance Feedback with Automatically Generated Query VariantsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412032(125-134)Online publication date: 19-Oct-2020
  • (2020)Venue Suggestion Using Social-Centric ScoresBias and Social Aspects in Search and Recommendation10.1007/978-3-030-52485-2_12(127-142)Online publication date: 12-Jul-2020
  • (2020)Joint Geographical and Temporal Modeling Based on Matrix Factorization for Point-of-Interest RecommendationAdvances in Information Retrieval10.1007/978-3-030-45439-5_14(205-219)Online publication date: 8-Apr-2020
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