Collaborative compound critiquing

Haoran XIE, Li CHEN, Feng WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

11 Citations (Scopus)


Critiquing-based recommender systems offer users a conversational paradigm to provide their feedback, named critiques, during the process of viewing the current recommendation. In this way, the system is able to learn and adapt to the users’ preferences more precisely so that better recommendation could be returned in the subsequent iteration. Moreover, recent works on experience-based critiquing have suggested the power of improving the recommendation efficiency by making use of relevant sessions from other users’ histories so as to save the active user’s interaction effort. In this paper, we present a novel approach to processing the history data and apply it to the compound critiquing system. Specifically, we develop a history-aware collaborative compound critiquing method based on preference-based compound critique generation and graph-based similar session identification. Through experiments on two data sets, we validate the outperforming efficiency of our proposed method in comparison to the other experience-based methods. In addition, we verify that incorporating user histories into compound critiquing system can be significantly more effective than the corresponding unit critiquing system.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings
EditorsPeter Dolog, Francesco Ricci, David Chin, Vania Dimitrova, Tsvi Kuflik, Geert-Jan Houben
PublisherSpringer-Verlag GmbH and Co. KG
Number of pages12
ISBN (Electronic)9783319087856
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Aalborg, Netherlands
Duration: 7 Jul 201411 Jul 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014


  • Conversational recommender systems
  • History-aware compound critiquing


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