Weighted sparse coding residual minimization for visual tracking
Junchi Yan, Minglei Tong
VCIP 2011
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.
Junchi Yan, Minglei Tong
VCIP 2011
Junchi Yan, Chao Zhang, et al.
AAAI/IAAI 2015
Junchi Yan, Hongteng Xu, et al.
ICCV 2015
Chao Zhang, Junchi Yan, et al.
ICWS 2017