TY - GEN
T1 - Collaborative filtering via different preference structures
AU - Liu, Shaowu
AU - Pang, Na
AU - Xu, Guandong
AU - Liu, Huan
N1 - Funding Information:
Acknowledgment. This work was partially supported by the Guangxi Key Laboratory of Trusted Software (No. KX201528).
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Recently, social network websites start to provide third-parity sign-in options via the OAuth 2.0 protocol. For example, users can login Netflix website using their Facebook accounts. By using this service, accounts of the same user are linked together, and so does their information. This fact provides an opportunity of creating more complete profiles of users, leading to improved recommender systems. However, user opinions distributed over different platforms are in different preference structures, such as ratings, rankings, pairwise comparisons, voting, etc. As existing collaborative filtering techniques assume the homogeneity of preference structure, it remains a challenge task of how to learn from different preference structures simultaneously. In this paper, we propose a fuzzy preference relation-based approach to enable collaborative filtering via different preference structures. Experiment results on public datasets demonstrate that our approach can effectively learn from different preference structures, and show strong resistance to noises and biases introduced by cross-structure preference learning.
AB - Recently, social network websites start to provide third-parity sign-in options via the OAuth 2.0 protocol. For example, users can login Netflix website using their Facebook accounts. By using this service, accounts of the same user are linked together, and so does their information. This fact provides an opportunity of creating more complete profiles of users, leading to improved recommender systems. However, user opinions distributed over different platforms are in different preference structures, such as ratings, rankings, pairwise comparisons, voting, etc. As existing collaborative filtering techniques assume the homogeneity of preference structure, it remains a challenge task of how to learn from different preference structures simultaneously. In this paper, we propose a fuzzy preference relation-based approach to enable collaborative filtering via different preference structures. Experiment results on public datasets demonstrate that our approach can effectively learn from different preference structures, and show strong resistance to noises and biases introduced by cross-structure preference learning.
KW - Data mining
KW - Pairwise preference
KW - Recommender system
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U2 - 10.1007/978-3-319-63558-3_26
DO - 10.1007/978-3-319-63558-3_26
M3 - Conference contribution
AN - SCOPUS:85028455430
SN - 9783319635576
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 309
EP - 321
BT - Knowledge Science, Engineering and Management - 10th International Conference, KSEM 2017, Proceedings
A2 - Zhang, Zili
A2 - Ge, Yong
A2 - Jin, Zhi
A2 - Li, Gang
A2 - Blumenstein, Michael
PB - Springer Verlag
T2 - 10th International Conference on Knowledge Science, Engineering and Management, KSEM 2017
Y2 - 19 August 2017 through 20 August 2017
ER -