Chao Zhang, Junchi Yan, et al.
Neurocomputing
New item or topic profiling and recommendation are useful yet challenging, especially in face of a 'cold-start' situation with sparse user-item ratings for the new arrivals. In this paper, a method of acquiring review opinions of the 'sentinel' users on the cold-start items is proposed to elicit those items' latent profiles, and thus both user-specific ratings and future popularity of the items can be predicted simultaneously. Specifically, such a joint prediction task is formulated as a two-stage optimization problem, and a sentinel user selection algorithm is devised to facilitate effective latent profiles extraction for both item ratings and popularity predictions. Experiments with microblogging and movie data sets corroborate that the proposed method is capable of mitigating the cold-start problem and it outperforms several competitive peer methods.
Chao Zhang, Junchi Yan, et al.
Neurocomputing
Junchi Yan, Chunhua Tian, et al.
SOLI 2012
Chao Chen, Dongsheng Li, et al.
IJCAI 2016
Shuai Xiao, Mehrdad Farajtabar, et al.
NeurIPS 2017