Title: The BellKor Solution to the Netflix Grand Prize
Abstract: This article describes part of our contribution to the “BellKor’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).
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
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Cited By Count: 328
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