Neural Factorization Machines for Predictive AnalyticsTechnology #2017-076
Questions about this technology? Ask a Technology Manager
- Prof Chua Tat Seng NExT Centre for Extreme Search E-mail: firstname.lastname@example.orgExternal Link (www.nextcenter.org)
- Managed By
- Ms Asha Srinivasan Associate Director (65)65161671
- Patent Protection
- US Patent Pending
Factorization Machines are a popular approach to designing a machine learning model that can learn feature interaction from raw data automatically. While FM has yielded great promise in many prediction tasks, its performance can be limited by its linearity, as well as the modelling of pairwise feature interactions only since most real-world data have complex and non-linear underlying structures.
Neural Factorization Machines (NFMs) is a novel model for predictive analytics with sparse inputs which enhances FMs by modelling higher-order and non-linear feature interactions that are closer to real-world data. NFM achieves this by devising a new operation in neural network modelling called Bilinear Interaction (Bi-Interaction) pooling, subsuming FM under the neural network framework for the first time.
Stage of Development
Analytical and laboratory studies to validate analytical predictions.
AFM will be particularly effective for prediction tasks involving categorical predictor variables, such as user/item IDs, attributes, tags and categories. As an example, it can be used in the ranking engine of recommendation systems and click-through-rate (CTR) prediction of online advertising systems.
In contrast to traditional deep learning
methods that simply concatenate or average embedding vectors at a low level,
the use of Bi-Interaction pooling encodes more informative feature
interactions, greatly facilitating the following "deep" layers to
learn meaningful information.
It allows the implementation and tuning of FM by using
various techniques developed for neural networks. For example, we can use dropout
and batch normalization for preventing overfitting of FM.
NFM can model higher-order and non-linear interactions
between features in the same efficiency level as FM.
Patent pending. Available for licensing.
Prof Chua Tat Seng
NExT Centre for Extreme Search
ILO Ref No: 2017-076