title: THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS
abstract: 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.
authors: Oleg Rybakov Vijai Mohan Avishkar Misra Scott LeGrand Rejith Joseph Kiuk Chung Siddharth Singh Qian You Eric Nalisnick Leo Dirac Runfei Luo
hIndex: Eric Nalisnick 15