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THE UNREASONABLE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS
Nov 03, 2017 (modified: Nov 03, 2017)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:We present a personalized recommender system using neural network for recommending
products, such as eBooks, audio-books (Audible), Mobile Apps, Video
and Music. It produces recommendations based on user consumption history: 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 network with two hidden layers can capture
seasonality changes, and at the same time outperforms other modeling techniques,
including our recommender in production. Most importantly, we demonstrate our
model with two hidden layers can be scaled to all digital categories. Finally, we
show online A/B test results, discuss key improvements to the neural network
model, and describe our production pipeline.
TL;DR:Improving recommendations using time sensitive modeling with neural networks in multiple product categories on a retail website
Keywords:Recommender systems, deep learning, personalization
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