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Short-video recommendation is one of the most important recommendationapplications in today's industrial information systems. Compared with otherrecommendation tasks, the enormous amount of feedback is the most typicalcharacteristic. Specifically, in short-video recommendation, theeasiest-to-collect user feedback is from the skipping behaviors, which leads totwo critical challenges for the recommendation model. First, the skippingbehavior reflects implicit user preferences, and thus it is challenging forinterest extraction. Second, the kind of special feedback involves multipleobjectives, such as total watching time, which is also very challenging. Inthis paper, we present our industrial solution in Kuaishou, which servesbillion-level users every day. Specifically, we deploy a feedback-awareencoding module which well extracts user preference taking the impact ofcontext into consideration. We further design a multi-objective predictionmodule which well distinguishes the relation and differences among differentmodel objectives in the short-video recommendation. We conduct extensive onlineA/B testing, along with detailed and careful analysis, which verifies theeffectiveness of our solution.

Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System