Abstract
Trust-aware recommender systems have received
much attention recently for their abilities to capture
the influence among connected users. However,
they suffer from the efficiency issue due to large
amount of data and time-consuming real-valued operations. Although existing discrete collaborative
filtering may alleviate this issue to some extent,
it is unable to accommodate social influence. In
this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we
map the latent representation of users and items
into a joint hamming space by recovering the rating
and trust interactions between users and items. We
adopt a sophisticated discrete coordinate descent
(DCD) approach to optimize our proposed model.
In addition, experiments on two real-world datasets
demonstrate the superiority of our approach against
other state-of-the-art approaches in terms of ranking accuracy and efficiency