THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS

Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric Nalisnick, Leo Dirac, Runfei Luo

Keywords: Recommender systems, deep learning, personalization

TL;DR: Improving recommendations using time sensitive modeling with neural networks in multiple product categories on a retail website

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.

Data: [MovieLens](https://paperswithcode.com/dataset/movielens)