On-Device Learning System for Lightweight Recommendation Models on Set-Top Box with ARM NEON Optimization

This article describes part of our contribution to the "Bell-Kor's Pragmatic Chaos" final solution, which won the Netflix Grand Prize.The other portion of the contribution was created while working at AT&T with Robert Bell and Chris Volinsky, as reported in our 2008 Progress Prize report [3].The final solution includes all the predictors described there.In this article we describe only the newer predictors.So what is new over last year's solution?First we further improved the baseline predictors (Sec.III).This in turn improves our other models, which incorporate those predictors, like the matrix factorization model (Sec.IV).In addition, an extension of the neighborhood model that addresses temporal dynamics was introduced (Sec.V).On the Restricted Boltzmann Machines (RBM) front, we use a new RBM model with superior accuracy by conditioning the visible units (Sec.VI).The final addition is the introduction of a new blending algorithm, which is based on gradient boosted decision trees (GBDT) (Sec.VII). II. PRELIMINARIESThe Netflix dataset contains more than 100 million datestamped movie ratings performed by anonymous Netflix customers between Dec 31, 1999 and Dec 31, 2005 [4].This dataset gives ratings about m = 480, 189 users and n = 17, 770 movies (aka, items).The contest was designed in a training-test set format.A Hold-out set of about 4.2 million ratings was created consisting of the last nine movies rated by each user (or fewer if a user had not rated at least 18 movies over the entire period).The remaining data made up the training set.The Hold-out set was randomly split three ways, into subsets called Probe, Quiz, and Test.The Probe set was attached to the training set, and labels (the rating that the user gave the movie) were attached.The Quiz and Test sets made up an evaluation set, which is known as the Qualifying set, that competitors were required to predict ratings for.Once a competitor submits predictions, the prizemaster returns the root mean squared error (RMSE) achieved on the Quiz set, which is posted on a public leaderboard (www.netflixprize.com/leaderboard).RMSE values mentioned in this article correspond to the Quiz set.Ultimately, the winner of the prize is the one that scores best on the Test set, and those scores were never disclosed by Netflix.This precludes clever systems which might "game" the competition by learning about the Quiz set through repeated submissions.Compared with the training data, the Hold-out set contains many more ratings by users that do not rate much and are Y.