An item-oriented recommendation algorithm on cold-start problem

Qiu, Tian; Chen, Guang; Zhang, Zi-Ke; Zhou, Tao

doi:10.1209/0295-5075/95/58003

Back

Journal article

An item-oriented recommendation algorithm on cold-start problem

Qiu, Tian School of Information Engineering, Nanchang Hangkong University - Nanchang, China
Chen, Guang School of Information Engineering, Nanchang Hangkong University - Nanchang, China
Zhang, Zi-Ke Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, China - Institute of Information Economy, Hangzhou Normal University - Hangzhou, China - Department of Physics, University of Fribourg, Switzerland
Zhou, Tao Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, China - Department of Modern Physics, University of Science and Technology of China - Hefei, China

18.08.2011

Published in:

EPL - Europhysics Letters. - 2011, vol. 95, no. 5, p. 58003

English Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language