research-article

Constructive Recommendation

Author:

Paolo DragoneAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 441 - 445

https://doi.org/10.1145/3109859.3109867

Published: 27 August 2017 Publication History

Abstract

Constructive recommendation is the task of recommending object "configurations", i.e. objects that can be assembled from their components on the basis of the user preferences. Examples include: PC configurations, recipes, travel plans, layouts, and other structured objects. Recommended objects are created by maximizing a learned utility function over an exponentially (or even infinitely) large combinatorial space of configurations. The utility function is learned through preference elicitation, an interactive process for collecting user feedback about recommended objects. Constructive recommendation systems can help users make good decisions over complex configuration spaces. Our goal is to adapt existing machine learning techniques, or devise new ones, in order to build recommendation systems suitable for constructive tasks.

References

[1]

G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, and S. V. N. Vishwanathan. 2007. Predicting Structured Data. (2007).

[2]

Craig Boutilier, Fahiem Bacchus, and Ronen I. Brafman. 2001. UCP-networks: A directed graphical representation of conditional utilities. In UAI. 56--64.

Digital Library

[3]

Hugh Chipman. 1996. Bayesian variable selection with related predictors. CJS 24, 1 (1996), 17--36.

[4]

Michael Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In ACL'02, Vol. 10. 1--8.

Digital Library

[5]

Paolo Dragone, Luca Erculiani, Maria Teresa Chietera, Stefano Teso, and Andrea Passerini. 2016. Constructive Layout Synthesis via Coactive Learning. In Constructive Machine Learning workshop, NIPS.

[6]

Alexander Felfernig and Robin Burke. 2008. Constraint-based recommender systems: technologies and research issues. In Proceedings of the 10th international conference on Electronic commerce. ACM, 3.

Digital Library

[7]

Shengbo Guo and Scott Sanner. 2010. Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries. In AISTATS. 289--296.

[8]

Jordan J. Louviere, David A. Hensher, and Joffre D. Swait. 2000. Stated choice methods: analysis and applications. Cambridge University Press.

Digital Library

[9]

Gabriella Pigozzi, Alexis Tsoukiàs, and Paolo Viappiani. 2016. Preferences in artificial intelligence. Annals of Mathematics and Artificial Intelligence (2016).

Digital Library

[10]

Pearl Pu and Li Chen. 2009. User-involved preference elicitation for product search and recommender systems. AI magazine 29, 4 (2009), 93.

[11]

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. Springer.

Digital Library

[12]

Ariel Rubinstein. 1998. Modeling bounded rationality. MIT press.

[13]

Pannaga Shivaswamy and Thorsten Joachims. 2015. Coactive Learning. JAIR 53 (2015), 1--40.

Digital Library

[14]

Stefano Teso, Paolo Dragone, and Andrea Passerini. 2017. Coactive Critiquing: Elicitation of Preferences and Features. In AAAI.

[15]

Stefano Teso, Andrea Passerini, and Paolo Viappiani. 2016. Constructive Preference Elicitation by Setwise Max-Margin Learning. In IJCAI. 2067--2073.

Digital Library

[16]

Olivier Toubia, John R. Hauser, and Duncan I. Simester. 2004. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research 41, 1 (2004), 116--131.

[17]

Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, and Yasemin Altun. 2004. Support vector machine learning for interdependent and structured output spaces. In ICML. ACM, 104.

Digital Library

[18]

Paolo Viappiani and Craig Boutilier. 2010. Optimal Bayesian recommendation sets and myopically optimal choice query sets. In NIPS. 2352--2360.

Digital Library

[19]

Martin Zinkevich. 2003. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th International Conference on Machine Learning (ICML-03). 928--936.

Digital Library

Cited By

Frost SMcCalla G(2020)A Planning Algorithm to Support Learning in Open-ended, Unstructured EnvironmentsInternational Journal of Artificial Intelligence in Education10.1007/s40593-020-00221-331:4(847-877)Online publication date: 20-Oct-2020
https://doi.org/10.1007/s40593-020-00221-3

Index Terms

Constructive Recommendation
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

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

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Cited By

Frost SMcCalla G(2020)A Planning Algorithm to Support Learning in Open-ended, Unstructured EnvironmentsInternational Journal of Artificial Intelligence in Education10.1007/s40593-020-00221-331:4(847-877)Online publication date: 20-Oct-2020
https://doi.org/10.1007/s40593-020-00221-3

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