poster

History-aware critiquing-based conversational recommendation

Authors:

Yasser Salem,

Jun HongAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 63 - 64

https://doi.org/10.1145/2487788.2487813

Published: 13 May 2013 Publication History

Get Access

Abstract

In this paper we present a new approach to critiquing-based conversational recommendation, which we call History-Aware Critiquing (HAC). It takes a case-based reasoning approach by reusing relevant recommendation sessions of past users to short-cut the recommendation session of the current user. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains similar recommended items to the ones in the current session and its critiques sufficiently overlap with the critiques so far in the current session. HAC extends experience-based critiquing (EBC).

Our experimental results show that, in terms of recommendation efficiency, while EBC performs better than standard critiquing (STD), it does not perform as well as more recent techniques such as incremental critiquing (IC), whereas HAC achieves better recommendation efficiency over both STD and IC.

References

[1]

R. Burke, K. Hammond, and B. Young. The FindMe Approach to Assisted Browsing. IEEE Expert, 12(4):32--40, 1997.

Digital Library

Google Scholar

[2]

K. McCarthy, Y. Salem, and B. Smyth. Experience-Based Critiquing: Reusing Critiquing Experiences to Improve Conversational Recommendation. In Proceedings of the 18th International Conference on Case-Based Reasoning, (ICCBR-2010), pages 480--494, 2010.

Digital Library

Google Scholar

[3]

J. Reilly, K. McCarthy, L. McGinty, and B. Smyth. Incremental critiquing. Knowledge-Based Systems, 18(4-5):143--151, 2005.

Digital Library

Google Scholar

Cited By

View all

Shalom ORoitman HKouki P(2021)Natural Language Processing for Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_12(447-483)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_12
Contreras DSalamó M(2020)Data-driven decision making in critique-based recommenders: from a critique to social media dataJournal of Intelligent Information Systems10.1007/s10844-018-0520-954:1(23-44)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10844-018-0520-9
Contreras DSalamó M(2018)A Cognitively Inspired Clustering Approach for Critique-Based RecommendersCognitive Computation10.1007/s12559-018-9586-512:2(428-441)Online publication date: 4-Aug-2018
https://doi.org/10.1007/s12559-018-9586-5
Show More Cited By

Index Terms

History-aware critiquing-based conversational recommendation
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

Recommendations

Latent Linear Critiquing for Conversational Recommender Systems
WWW '20: Proceedings of The Web Conference 2020

Critiquing is a method for conversational recommendation that iteratively adapts recommendations in response to user preference feedback. In this setting, a user is iteratively provided with an item recommendation and attribute description for that item;...
History-guided conversational recommendation
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Product recommendation is an important aspect of many e-commerce systems. It provides an effective way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders. We present a new critiquing-based approach, ...
Conversational Collaborative Recommendation --- An Experimental Analysis

Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user's (past) stored preferences. However, content-based recommender system research ...

Comments

Information & Contributors

Information

Published In

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

Check for updates

Author Tags

Qualifiers

Poster

Conference

WWW '13

Sponsor:

NICBR
CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

Acceptance Rates

WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
275
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Shalom ORoitman HKouki P(2021)Natural Language Processing for Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_12(447-483)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_12
Contreras DSalamó M(2020)Data-driven decision making in critique-based recommenders: from a critique to social media dataJournal of Intelligent Information Systems10.1007/s10844-018-0520-954:1(23-44)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10844-018-0520-9
Contreras DSalamó M(2018)A Cognitively Inspired Clustering Approach for Critique-Based RecommendersCognitive Computation10.1007/s12559-018-9586-512:2(428-441)Online publication date: 4-Aug-2018
https://doi.org/10.1007/s12559-018-9586-5
Xie HWang DRao YWong TRaymond LChen LWang F(2016)Incorporating user experience into critiquing-based recommender systems: a collaborative approach based on compound critiquingInternational Journal of Machine Learning and Cybernetics10.1007/s13042-016-0611-29:5(837-852)Online publication date: 3-Oct-2016
https://doi.org/10.1007/s13042-016-0611-2
Salem YHong JLiu W(2015)CSFinderProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7363813(687-696)Online publication date: 29-Oct-2015
https://dl.acm.org/doi/10.1109/BigData.2015.7363813
Vasudevan SChakraborti SChung CBroder AShim KSuel T(2014)Mining user trails in critiquing based recommendersProceedings of the 23rd International Conference on World Wide Web10.1145/2567948.2579238(777-780)Online publication date: 7-Apr-2014
https://dl.acm.org/doi/10.1145/2567948.2579238
Salem YHong JLiu WChung CBroder AShim KSuel T(2014)History-guided conversational recommendationProceedings of the 23rd International Conference on World Wide Web10.1145/2567948.2578844(999-1004)Online publication date: 7-Apr-2014
https://dl.acm.org/doi/10.1145/2567948.2578844
Vasudevan SChakraborti S(2014)Enriching Case Descriptions Using Trails in Conversational RecommendersCase-Based Reasoning Research and Development10.1007/978-3-319-11209-1_34(480-494)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11209-1_34
Xie HChen LWang F(2014)Collaborative Compound CritiquingUser Modeling, Adaptation, and Personalization10.1007/978-3-319-08786-3_22(254-265)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08786-3_22

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Latent Linear Critiquing for Conversational Recommender Systems

History-guided conversational recommendation

Conversational Collaborative Recommendation --- An Experimental Analysis