Addressing the Conflict of Negative Feedback and Sampling for Online Ad Recommendation in Mobile Social Networks

Online advertisement (ad) recommendation in the mobile social network (MSN) is an uprising interest of research. Compared to traditional recommendation systems, one of its major difference is the presence of explicit negative feedback from users (e.g., a user does not click an ad, or she/he does not like it). On the other hand, most methods utilize negative sampling (e.g., randomly sampling an item that a user never interacts with to avoid overfitting, that is, she/he is assumed to dislike it) while training conventional recommendation systems. This may lead to a conflict between negative feedback and sampling, as they should be treated differently, but they are considered as the same if traditional methods are directly applied for online ad recommendation. In this paper, we present AdRec, a novel framework of online ad recommendation in MSN to address this conflict. We introduce an auxiliary output and modify the loss function to assign different weights to negative samples and feedbacks. A theoretical analysis is applied to show the efficiency of our design, and experiments on real world datasets demonstrate that our proposed method outperforms several stateof-the-art approaches.