A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is
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, calleddoi:10.1145/2939672.2939714 dblp:conf/kdd/TanFLWLLPXH16 fatcat:xmeo3bfzxjbrpei4u47sgrrwti