1,258 research outputs found
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation
Exponential Machines
Modeling interactions between features improves the performance of machine
learning solutions in many domains (e.g. recommender systems or sentiment
analysis). In this paper, we introduce Exponential Machines (ExM), a predictor
that models all interactions of every order. The key idea is to represent an
exponentially large tensor of parameters in a factorized format called Tensor
Train (TT). The Tensor Train format regularizes the model and lets you control
the number of underlying parameters. To train the model, we develop a
stochastic Riemannian optimization procedure, which allows us to fit tensors
with 2^160 entries. We show that the model achieves state-of-the-art
performance on synthetic data with high-order interactions and that it works on
par with high-order factorization machines on a recommender system dataset
MovieLens 100K.Comment: ICLR-2017 workshop track pape
Applying Deep Learning To Airbnb Search
The application to search ranking is one of the biggest machine learning
success stories at Airbnb. Much of the initial gains were driven by a gradient
boosted decision tree model. The gains, however, plateaued over time. This
paper discusses the work done in applying neural networks in an attempt to
break out of that plateau. We present our perspective not with the intention of
pushing the frontier of new modeling techniques. Instead, ours is a story of
the elements we found useful in applying neural networks to a real life
product. Deep learning was steep learning for us. To other teams embarking on
similar journeys, we hope an account of our struggles and triumphs will provide
some useful pointers. Bon voyage!Comment: 8 page
- …