Audience Activity Recommendation Using Stacked-LSTM Based Sequence Learning
Syed Tanveer Jishan, Yiji Wang · 2017
Recommender systems are used to suggest products to audiences by employing a similarity metric. One of the problem of such systems is that it does not incorporate the context of time. As result, it is not possible to change recommendation as audiences' preferences changes over time. In this paper, we will be presenting a solution based on recurrent neural network to alleviate this problem and highlight a use case on how recurrent neural network model can help us build a real-time recommender system. We will also discuss regarding the comparative study on the performance of different types of recurrent neural network models for the recommendation task.