Accepted Papers

Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework

Xiaoyang Ma, Lan Zhang, Lan Xu, Zhicheng Liu, Ge Chen, Zhili Xiao, Yang Wang and Zhengtao Wu


Understanding and forecasting user visits is of great importance for a variety of tasks, e.g., online advertising, which is one of the most profitable business models for Internet services. Publishers sell advertising spaces in advance with user visit volume and attributes guarantees. There are usually tens of thousands of attribute combinations in an online advertising system. The key problem is how to accurately forecast the number of user visits for each attribute combination. Many traditional work characterizing temporal trends of every single time series are quite inefficient for large-scale time series. Recently, a number of models based on deep learning or matrix factorization have been proposed for high-dimensional time series forecasting. However, most of them neglect correlations among attribute combinations, or are tailored for specific applications, resulting in poor adaptability for different business scenarios.Besides, sophisticated deep learning models usually cause high time and space complexity. There is still a lack of an efficient highly scalable and adaptable solution for accurate high-dimensional time series forecasting. To address this issue, in this work, we conduct a thorough analysis on large-scale user visits data and propose a novel deep spatial-temporal tensor factorization framework, which provides a general design for high-dimensional time series forecasting. We deployed the proposed framework in Tencent online guaranteed delivery advertising system, and extensively evaluated the effectiveness and efficiency of the framework in two different large-scale application scenarios. The results show that our framework outperforms existing methods in prediction accuracy. Meanwhile, it significantly reduces the parameter number and is resistant to incomplete data with up to 20% missing values.

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