TY - GEN
T1 - Collaborative compound critiquing
AU - XIE, Haoran
AU - CHEN, Li
AU - WANG, Feng
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Conversational recommender systems
KW - History-aware compound critiquing
UR - http://www.scopus.com/inward/record.url?scp=84927539311&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08786-3_22
DO - 10.1007/978-3-319-08786-3_22
M3 - Conference paper (refereed)
AN - SCOPUS:84927539311
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 265
BT - User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP 2014, Proceedings
A2 - Dolog, Peter
A2 - Ricci, Francesco
A2 - Chin, David
A2 - Dimitrova, Vania
A2 - Kuflik, Tsvi
A2 - Houben, Geert-Jan
PB - Springer-Verlag GmbH and Co. KG
T2 - 22nd International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014
Y2 - 7 July 2014 through 11 July 2014
ER -