Joint prediction of rating and popularity for cold-start item by sentinel user selection

Abstract

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.