Workshop track -ICLR 2018 THE EFFECTIVENESS OF A TWO-LAYER NEURAL NET- WORK FOR RECOMMENDATIONS
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.
The paper proposes a new neural network based method for recommendation.
The main finding of the paper is that a relatively simple method works for recommendation, compared to other methods based on neural networks that have been recently proposed.
This contribution is not bad for an empirical paper. There's certainly not that much here that's groundbreaking methodologically, though it's certainly nice to know that a simple and scalable method works.
There's not much detail about the data (it is after all an industrial paper). It would certainly be helpful to know how well the proposed method performs on a few standard recommender systems benchmark datasets (compared to the same baselines), in order to get a sense as to whether the improvement is actually due to having a better model, versus being due to some unique attributes of this particular industrial dataset under consideration. As it is, I am a little concerned that this may be a method that happens to work well for the types of data the authors are considering but may not work elsewhere.
Other than that, it's nice to see an evaluation on real production data, and it's nice that the authors have provided enough info that the method should be (more or less) reproducible. There's some slight concern that maybe this paper would be better for the industry track of some conference, given that it's focused on an empirical evaluation rather than really making much of a methodological contribution. Again, this could be somewhat alleviated by evaluating on some standard and reproducible benchmarks.