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A Package Recommendation Framework for Trip Planning Activities

Published: 07 September 2016 Publication History

Abstract

Classical recommender systems provide users with ranked lists of recommendations, where each one consists of a single item. However, these ranked lists are not suitable for applications such as trip planning, which deal with heterogeneous items. In this paper, we focus on the problem of recommending a set of packages to the user, where each package is constituted with a set of different Points of Interest that may constitute a tour. Given a collection of POIs, our goal is to recommend the most interesting packages for the user, where each package satisfies the budget constraints. We formally define the problem and we present a novel composite recommendation system, inspired from composite retrieval. Experimental evaluation of our proposed system, using a real-world dataset demonstrates its quality and its ability to improve both diversity and relevance of recommendations.

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References

[1]
S. Amer-Yahia, F. Bonchi, C. Castillo, E. Feuerstein, I. Mendez-Diaz, and P. Zabala. Composite retrieval of diverse and complementary bundles. Transactions on Knowledge and Data Engineering, 2014.
[2]
A. Angel, S. Chaudhuri, G. Das, and N. Koudas. Ranking objects based on relationships and fixed associations. EDBT, 2009.
[3]
L. Castillo, E. Armengol, E. Onaindıa, L. Sebastiá, J. González-Boticario, A. Rodrıguez, S. Fernández, J. D. Arias, and D. Borrajo. Samap: An user-oriented adaptive system for planning tourist visits. Expert Systems with Applications, 2008.
[4]
M. De Choudhury, M. Feldman, S. Amer-Yahia, N. Golbandi, R. Lempel, and C. Yu. Automatic construction of travel itineraries using social breadcrumbs. In Proceedings of the 21st ACM conference on Hypertext and hypermedia, 2010.
[5]
H. Steck. Item popularity and recommendation accuracy. RecSys, 2011.
[6]
M. Xie, L. V. Lakshmanan, and P. T. Wood. Breaking out of the box of recommendations: From items to packages. RecSys, 2010.
[7]
M. Xie, L. V. Lakshmanan, and P. T. Wood. Comprec-trip: A composite recommendation system for travel planning. In Data Engineering (ICDE), 2011 IEEE 27th International Conference on, 2011.
[8]
C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In World Wide Web, 2005.

Cited By

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  • (2025)Multi-view graph contrastive representation learning for bundle recommendationInformation Processing & Management10.1016/j.ipm.2024.10395662:1(103956)Online publication date: Jan-2025
  • (2025)Cruise onboard itinerary planning for multi passengers with service venue capacity and time-window constraintsComputers and Operations Research10.1016/j.cor.2024.106944176:COnline publication date: 20-Feb-2025
  • (2024)MultiCBR: Multi-view Contrastive Learning for Bundle RecommendationACM Transactions on Information Systems10.1145/3640810Online publication date: 23-Jan-2024
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cover image ACM Conferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
September 2016
490 pages
ISBN:9781450340359
DOI:10.1145/2959100
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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New York, NY, United States

Publication History

Published: 07 September 2016

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Author Tags

  1. diversity
  2. package
  3. recommender system
  4. top-k
  5. trip planning

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  • Short-paper

Funding Sources

  • Région Picardie

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RecSys '16
Sponsor:
RecSys '16: Tenth ACM Conference on Recommender Systems
September 15 - 19, 2016
Massachusetts, Boston, USA

Acceptance Rates

RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2025)Multi-view graph contrastive representation learning for bundle recommendationInformation Processing & Management10.1016/j.ipm.2024.10395662:1(103956)Online publication date: Jan-2025
  • (2025)Cruise onboard itinerary planning for multi passengers with service venue capacity and time-window constraintsComputers and Operations Research10.1016/j.cor.2024.106944176:COnline publication date: 20-Feb-2025
  • (2024)MultiCBR: Multi-view Contrastive Learning for Bundle RecommendationACM Transactions on Information Systems10.1145/3640810Online publication date: 23-Jan-2024
  • (2024)Encoder-Decoder Based Route Generation Model for Flexible Travel RecommendationIEEE Transactions on Services Computing10.1109/TSC.2024.337623117:3(905-920)Online publication date: May-2024
  • (2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
  • (2024)Recommendation rules to personalize itineraries for tourists in an unfamiliar cityApplied Soft Computing10.1016/j.asoc.2023.111084150(111084)Online publication date: Jan-2024
  • (2024)The effect of the product categories diversity recommended on cross‐buying in electronic commerce platforms: The moderating role of user navigation heterogeneityJournal of Consumer Behaviour10.1002/cb.239324:1(304-331)Online publication date: 25-Oct-2024
  • (2023)Complex Item Set RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594248(3444-3447)Online publication date: 19-Jul-2023
  • (2023)Revenue Maximization: The Interplay Between Personalized Bundle Recommendation and Wireless Content CachingIEEE Transactions on Mobile Computing10.1109/TMC.2022.314280922:7(4253-4265)Online publication date: 1-Jul-2023
  • (2023)DeepAltTrip: Top-K Alternative Itineraries for Trip RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323959535:9(9433-9447)Online publication date: 1-Sep-2023
  • Show More Cited By

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