research-article

Breaking out of the box of recommendations: from items to packages

Authors:

Laks V.S. Lakshmanan,

Peter T. WoodAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 151 - 158

https://doi.org/10.1145/1864708.1864739

Published: 26 September 2010 Publication History

Abstract

Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend top-k packages for the user to choose from.

Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender system, focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of generating the top recommendation (package) is NP-complete, we devise several approximation algorithms for generating top-k packages as recommendations. We analyze their efficiency as well as approximation quality. Finally, using two real and two synthetic data sets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for top-k packages compared to exact algorithms.

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Cited By

Robbi EBronzini MViappiani PPasserini A(2024)Personalized bundle recommendation using preference elicitation and the Choquet integralFrontiers in Artificial Intelligence10.3389/frai.2024.13466847Online publication date: 14-Feb-2024
https://doi.org/10.3389/frai.2024.1346684
Sun ZFeng KYang JFang HQu XOng YLiu W(2024)Revisiting Bundle Recommendation for Intent-aware Product BundlingACM Transactions on Recommender Systems10.1145/36528652:3(1-34)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3652865
Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
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Index Terms

Breaking out of the box of recommendations: from items to packages
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2010 ACM.

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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Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Robbi EBronzini MViappiani PPasserini A(2024)Personalized bundle recommendation using preference elicitation and the Choquet integralFrontiers in Artificial Intelligence10.3389/frai.2024.13466847Online publication date: 14-Feb-2024
https://doi.org/10.3389/frai.2024.1346684
Sun ZFeng KYang JFang HQu XOng YLiu W(2024)Revisiting Bundle Recommendation for Intent-aware Product BundlingACM Transactions on Recommender Systems10.1145/36528652:3(1-34)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3652865
Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
Zou DZhao SWei WMao XLi RChen DFang RFu Y(2024)Towards Hierarchical Intent Disentanglement for Bundle RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3329175(1-12)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3329175
Liu XYuan M(2023)RETRACTED: A Global Structural Hypergraph Convolutional Model for Bundle RecommendationElectronics10.3390/electronics1218395212:18(3952)Online publication date: 19-Sep-2023
https://doi.org/10.3390/electronics12183952
Wei WZhao SZou D(2023)Recommendation System: A Survey and New PerspectivesWorld Scientific Annual Review of Artificial Intelligence10.1142/S281103232330001301Online publication date: 4-May-2023
https://doi.org/10.1142/S2811032323300013
Shakeri MMiahi EGupta AOng Y(2023)Scalable Transfer Evolutionary Optimization: Coping With Big Task InstancesIEEE Transactions on Cybernetics10.1109/TCYB.2022.316439953:10(6160-6172)Online publication date: Oct-2023
https://doi.org/10.1109/TCYB.2022.3164399
Felfernig AAtas MSamer RStettinger MTran TReiterer S(2023)Further Choice ScenariosGroup Recommender Systems10.1007/978-3-031-44943-7_7(135-151)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_7
Sato RSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Enumerating Fair Packages for Group RecommendationsProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498432(870-878)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498432
Cong ZChu LYang YPei J(2021)Comprehensible counterfactual explanation on Kolmogorov-Smirnov testProceedings of the VLDB Endowment10.14778/3461535.346154614:9(1583-1596)Online publication date: 22-Oct-2021
https://dl.acm.org/doi/10.14778/3461535.3461546
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