research-article

ReComment: towards critiquing-based recommendation with speech interaction

Authors:

Alexander Felfernig,

Florian ReinfrankAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 157 - 164

https://doi.org/10.1145/2507157.2507161

Published: 12 October 2013 Publication History

Abstract

In contrast to search-based approaches, critiquing-based recommender systems provide a navigation-based interface where users are enabled to critique displayed recommendations as a means of preference elicitation. In this paper we present Recomment, our approach to natural language based unit critiquing. We discuss the developed prototype and present the corresponding user interface. In order to show the applicability of our concepts, we present the results of a user study. This study shows that speech interfaces have the potential to improve the perceived ease of use as well as the overall quality of recommendations.

References

[1]

D. Bridge. Towards conversational recommender systems: A dialogue grammar approach. In Proceedings of the Workshop in Mixed-Initiative Case-Based Reasoning, Workshop Prog. at the 6th Europ. Conf. in CBR, pages 9--22, 2002.

[2]

E. Brill and R. J. Mooney. An overview of empirical natural language processing. AI magazine, 18(4):13, 1997.

Digital Library

[3]

J. Brooke. Sus-a quick and dirty usability scale. Usability evaluation in industry, 189--194, 1996.

[4]

R. Burke. Knowledge-based recommender systems. In Encyclopedia of Library and Information Systems. Marcel Dekker, 2000.

[5]

R. D. Burke, K. J. Hammond, and B. Yound. The findme approach to assisted browsing. IEEE Expert, 12(4):32--40, 1997.

Digital Library

[6]

R. D. Burke, K. J. Hammond, and B. C. Young. Knowledge-based navigation of complex information spaces. In Proceedings of the national conference on artificial intelligence, volume 462, page 468, 1996.

Digital Library

[7]

L. Chen and P. Pu. Evaluating critiquing-based recommender agents. In Proceedings of the National Conference on Artificial Intelligence, volume 21, page 157. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2006.

Digital Library

[8]

L. Chen and P. Pu. Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction, 22(1--2):125--150, 2012.

Digital Library

[9]

R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor. Emotion recognition in human-computer interaction. Signal Processing Magazine, IEEE, 18(1):32--80, 2001.

[10]

S.-J. Doh. Enhancements to transformation-based speaker adaptation: principal component and inter-class maximum likelihood linear regression. PhD thesis, Carnegie Mellon University, 2000.

[11]

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

Digital Library

[12]

M. Mandl and A. Felfernig. Improving the performance of unit critiquing. In User Modeling, Adaptation, and Personalization, pages 176--187. Springer, 2012.

Digital Library

[13]

K. McCarthy, L. McGinty, and B. Smyth. Dynamic critiquing: An analysis of cognitive load. In Proceedings of the 16th Irish Conference on Artificial Intelligence and Cognitive Science, pages 19--28, 2005.

[14]

K. McCarthy, J. Reilly, L. McGinty, and B. Smyth. On the dynamic generation of compound critiques in conversational recommender systems. In Adaptive Hypermedia and Adaptive Web-Based Systems, pages 176--184. Springer, 2004.

[15]

K. McCarthy, Y. Salem, and B. Smyth. Experience-based critiquing: reusing critiquing experiences to improve conversational recommendation. In Case-Based Reasoning. Research and Development, pages 480--494. Springer, 2010.

Digital Library

[16]

L. McGinty and J. Reilly. On the evolution of critiquing recommenders. In Recommender Systems Handbook, pages 419--453. Springer, 2011.

[17]

D. McSherry and D. W. Aha. The ins and outs of critiquing. In IJCAI, pages 962--967, 2007.

Digital Library

[18]

P. H. Z. Pu and P. Kumar. Evaluating example-based search tools. In Proceedings of the 5th ACM conference on Electronic commerce, pages 208--217. ACM, 2004.

Digital Library

[19]

L. Qiu and I. Benbasat. An investigation into the effects of text-to-speech voice and 3d avatars on the perception of presence and flow of live help in electronic commerce. ACM Trans. Comput.-Hum. Interact., 12(4):329--355, 2005.

Digital Library

[20]

J. Reilly, K. McCarthy, L. McGinty, and B. Smyth. Explaining compound critiques. Artificial Intelligence Review, 24(2):199--220, 2005.

Digital Library

[21]

J. Reilly, K. McCarthy, L. McGinty, and B. Smyth. Incremental critiquing. Knowledge-Based Systems, 18(4):143--151, 2005.

Digital Library

[22]

J. Reilly, J. Zhang, L. McGinty, P. Pu, and B. Smyth. Evaluating compound critiquing recommenders: a real-user study. In Proceedings of the 8th ACM conference on Electronic commerce, pages 114--123. ACM, 2007.

Digital Library

[23]

E. Shriberg. Toerrrr'is human: ecology and acoustics of speech disfluencies. Journal of the International Phonetic Association, 31(1):153--169, 2001.

[24]

C. A. Thompson, M. H. Goeker, and P. Langley. A personalized system for conversational recommendations. J. Artif. Intell. Res. (JAIR), 21:393--428, 2004.

Digital Library

[25]

J. Zhang and P. Pu. A comparative study of compound critique generation in conversational recommender systems. In Adaptive Hypermedia and Adaptive Web-Based Systems, pages 234--243. Springer, 2006.

Digital Library

Cited By

Uta MFelfernig ALe VTran TGarber DLubos SBurgstaller T(2024)Knowledge-based recommender systems: overview and research directionsFrontiers in Big Data10.3389/fdata.2024.13044397Online publication date: 26-Feb-2024
https://doi.org/10.3389/fdata.2024.1304439
Zhao YJaber RMcMillan DMunteanu C(2022)“Rewind to the Jiggling Meat Part”: Understanding Voice Control of Instructional Videos in Everyday TasksProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502036(1-11)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502036
Lan JShi RCao YLv J(2022)Knowledge Graph-based Conversational Recommender System in Travel2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892176(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892176
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Index Terms

ReComment: towards critiquing-based recommendation with speech interaction

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RecomMetz

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In this article, we propose 1 a knowledge-based probabilistic collaborative filtering CF recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation LDA model-based recommendation technique and 2 a ...
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HLT-NAACL-Short '04: Proceedings of HLT-NAACL 2004: Short Papers

Spoken user interfaces are conventionally either dialogue-based or menu-based. In this paper we propose a third approach, in which the task of invoking responses from the system is treated as one of retrieval from the set of all possible responses. ...

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Information & Contributors

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

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Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

Uta MFelfernig ALe VTran TGarber DLubos SBurgstaller T(2024)Knowledge-based recommender systems: overview and research directionsFrontiers in Big Data10.3389/fdata.2024.13044397Online publication date: 26-Feb-2024
https://doi.org/10.3389/fdata.2024.1304439
Zhao YJaber RMcMillan DMunteanu C(2022)“Rewind to the Jiggling Meat Part”: Understanding Voice Control of Instructional Videos in Everyday TasksProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502036(1-11)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502036
Lan JShi RCao YLv J(2022)Knowledge Graph-based Conversational Recommender System in Travel2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892176(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892176
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
Modesto de Araujo LTeles de Oliveira Cda Hora Rodrigues KGarcia Manzato M(2022)Empirical Studies Aimed at Understanding Conversational Recommender Systems and Accessibility AspectsHCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging10.1007/978-3-031-17902-0_33(462-478)Online publication date: 16-Oct-2022
https://doi.org/10.1007/978-3-031-17902-0_33
Kuanr MMohapatra P(2021)Assessment Methods for Evaluation of Recommender Systems: A SurveyFoundations of Computing and Decision Sciences10.2478/fcds-2021-002346:4(393-421)Online publication date: 17-Dec-2021
https://doi.org/10.2478/fcds-2021-0023
Najafian SDraws TBarile FTkalcic MYang JTintarev NConlan OHerder E(2021)Exploring User Concerns about Disclosing Location and Emotion Information in Group RecommendationsProceedings of the 32nd ACM Conference on Hypertext and Social Media10.1145/3465336.3475104(155-164)Online publication date: 30-Aug-2021
https://dl.acm.org/doi/10.1145/3465336.3475104
Manzoor AJannach D(2021)Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric ComparisonProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3475942(515-520)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3475942
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
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