Article

Conversational recommenders with adaptive suggestions

Authors:

Paolo Viappiani,

Boi FaltingsAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 89 - 96

https://doi.org/10.1145/1297231.1297246

Published: 19 October 2007 Publication History

Abstract

We consider a conversational recommender system based on example-critiquing where some recommendations are suggestions aimed at stimulating preference expression to acquire an accurate preference model. User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32].

This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples.

We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.

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Cited By

Zou JSun ALong CKanoulas E(2024)Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential ModelingACM Transactions on Information Systems10.1145/367737642:6(1-27)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3677376
Di Noia TDonini FJannach DNarducci FPomo C(2022)Conversational recommendationInformation Sciences: an International Journal10.1016/j.ins.2022.07.169614:C(325-347)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.07.169
Jannach D(2022)Evaluating conversational recommender systemsArtificial Intelligence Review10.1007/s10462-022-10229-x56:3(2365-2400)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s10462-022-10229-x
Show More Cited By

Index Terms

Conversational recommenders with adaptive suggestions
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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Published In

cover image ACM Conferences

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Copyright © 2007 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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Publication History

Published: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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Cited By

Zou JSun ALong CKanoulas E(2024)Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential ModelingACM Transactions on Information Systems10.1145/367737642:6(1-27)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3677376
Di Noia TDonini FJannach DNarducci FPomo C(2022)Conversational recommendationInformation Sciences: an International Journal10.1016/j.ins.2022.07.169614:C(325-347)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.07.169
Jannach D(2022)Evaluating conversational recommender systemsArtificial Intelligence Review10.1007/s10462-022-10229-x56:3(2365-2400)Online publication date: 12-Jul-2022
https://doi.org/10.1007/s10462-022-10229-x
Jannach DManzoor ACai WChen L(2021)A Survey on Conversational Recommender SystemsACM Computing Surveys10.1145/345315454:5(1-36)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3453154
Dragone PPellegrini GVescovi MTentori KPasserini APera SEkstrand MAmatriain XO'Donovan J(2018)No more ready-made dealsProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240348(163-171)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240348
Martins DVossen GMaleszka M(2018)Supporting Online Data Purchase by Preference Recommendation2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00627(3703-3708)Online publication date: Oct-2018
https://doi.org/10.1109/SMC.2018.00627
Martins DVossen Gde Lima Neto FSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)Intelligent decision support for data purchaseProceedings of the International Conference on Web Intelligence10.1145/3106426.3106434(396-402)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106434
Martins DVossen Gde Lima Neto F(2017)Learning database queries via intelligent semiotic machines2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)10.1109/LA-CCI.2017.8285698(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/LA-CCI.2017.8285698
Adomavicius GKwon Y(2015)Multi-Criteria Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_25(847-880)Online publication date: 2015
https://doi.org/10.1007/978-1-4899-7637-6_25
Blanco HRicci FShin SMaldonado J(2013)Inferring user utility for query revision recommendationProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480416(245-252)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2480362.2480416
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