LT4REC:A Lottery Ticket Hypothesis Based Multi-task Practice for Video Recommendation System
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by
Xuanji Xiao, Huabin Chen, Yuzhen Liu, Xing Yao, Pei Liu, Chaosheng Fan, Nian Ji, Xirong Jiang
2021
Abstract
Click-through rate prediction (CTR) and post-click conversion rate prediction
(CVR) play key roles across all industrial ranking systems, such as
recommendation systems, online advertising, and search engines. Different from
the extensive research on CTR, there is much less research on CVR estimation,
whose main challenge is extreme data sparsity with one or two orders of
magnitude reduction in the number of samples than CTR. People try to solve this
problem with the paradigm of multi-task learning with the sufficient samples of
CTR, but the typical hard sharing method can't effectively solve this problem,
because it is difficult to analyze which parts of network components can be
shared and which parts are in conflict, i.e., there is a large inaccuracy with
artificially designed neurons sharing. In this paper, we model CVR in a
brand-new method by adopting the lottery-ticket-hypothesis-based sparse sharing
multi-task learning, which can automatically and flexibly learn which neuron
weights to be shared without artificial experience. Experiments on the dataset
gathered from traffic logs of Tencent video's recommendation system demonstrate
that sparse sharing in the CVR model significantly outperforms competitive
methods. Due to the nature of weight sparsity in sparse sharing, it can also
significantly reduce computational complexity and memory usage which are very
important in the industrial recommendation system.
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