We present a personalized recommender system using neural network for recommending
 products, such as eBooks, audio-books, Mobile Apps, Video and Music.
 It produces recommendations based on customer’s implicit feedback history such
 as purchases, listens or watches. Our key contribution is to formulate recommendation
 problem as a model that encodes historical behavior to predict the future
 behavior using soft data split, combining predictor and auto-encoder models. We
 introduce convolutional layer for learning the importance (time decay) of the purchases
 depending on their purchase date and demonstrate that the shape of the time
 decay function can be well approximated by a parametrical function. We present
 offline experimental results showing that neural networks with two hidden layers
 can capture seasonality changes, and at the same time outperform other modeling
 techniques, including our recommender in production. Most importantly, we
 demonstrate that our model can be scaled to all digital categories, and we observe
 significant improvements in an online A/B test. We also discuss key enhancements
 to the neural network model and describe our production pipeline. Finally
 we open-sourced our deep learning library which supports multi-gpu model parallel
 training. This is an important feature in building neural network based recommenders
 with large dimensionality of input and output data.
Improving recommendations using time sensitive modeling with neural networks in multiple product categories on a retail website The paper proposes a new neural network based method for recommendation. The authors describe a procedure of building their production recommender system from scratch and integrate time decay of purchases into the learning framework.