Enhanced pairwise learning for personalized ranking from implicit feedback

Zhang, Yunzhou1,2; Yuan, Bo1,2; Tang, Ke1,2

10.1007/978-981-10-7179-9_46

2017

1865-0929

12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

791

580-595

Harbin, China

Springer Verlag

One-class collaborative filtering with implicit feedback has attracted much attention, mainly due to the widespread of implicit data in real world. Pairwise methods have been shown to be the state-of-the-art methods for one-class collaborative filtering, but the assumption that users prefer observed items to unobserved items may not always hold. Besides, existing pairwise methods may not perform well in terms of Top-N recommendation. In this paper, we propose a new approach called EBPR, which relaxes the former simple pairwise preference assumption by further exploiting the hidden connection in observed items and unobserved items. EBPR can also be used as a basic method and has the extensive applicability, i.e., when combining our model with former pairwise methods, better performance can also be achieved. Empirical studies show that our algorithm outperforms the state-of-the-art methods on four real-world datasets.
© Springer Nature Singapore Pte Ltd 2017.

通讯

EI

Ministry of Science and Technology of the People's Republic of China[2017YFC0804003]

20174704440186

Computation theory

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Information Sources and Analysis:903.1

EV Compendex

1.Department of Computer Science and Technology, USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications, University of Science and Technology of China, Hefei, China
2.Shenzhen Key Laboratory of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Zhang, Yunzhou,Yuan, Bo,Tang, Ke. Enhanced pairwise learning for personalized ranking from implicit feedback[C]:Springer Verlag,2017:580-595.