Scalable Time-Decaying Adaptive Prediction Algorithm

Yinyan Tan, Zhe Fan, Guilin Li, Fangshan Wang, Zhengbing Li, Shikai Liu, Qiuling Pan, Eric P. Xing, Qirong Ho

Abstract: Online learning is used in a wide range of real applications, e.g., predicting ad click-through rates (CTR) and personalized recommendations. Based on the analysis of users' behaviors in Video-On-Demand (VoD) recommender systems,we discover that the most recent users' actions can better reflect users' current intentions and preferences. Under this observation, we thereby propose a novel time-decaying online learning algorithm derived from the state-of-the-art FTRL-proximal algorithm, called Time-Decaying Adaptive Prediction (TDAP) algorithm. To scale Big Data, we further parallelize our algorithm following the data parallel scheme under both BSP and SSP consistency model. We experimentally evaluate our TDAP algorithm on real IPTV VoD datasets using two state-of-the-art distributed computing platforms, TDAP achieves good accuracy: it improves at least 5.6% in terms of prediction accuracy, compared to FTRL-proximal algorithm; and TDAP scales well: it runs 4 times faster when the number of machines increases from 2 to 10.