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High Performance supervised learning for recommendation and prediction

Technology #2017-075

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Researchers
Prof Chua Tat Seng
NExT Centre for Extreme Search E-mail: dcscts@comp.nus.edu.sg
External Link (www.nextcenter.org)
Managed By
Ms Asha Srinivasan
Associate Director (65)65161671
Patent Protection

US Patent Pending
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Tech Offer 2017-075 Attentional Factorization [PDF]

AFM: High performance supervised learning method for recommendation and prediction

Market Opportunity

Factorization machines (FM) are a new model class that combine the advantages of support vector machines, one of the most popular predictors in machine learning, data mining and factorization models. FMs are mainly used for supervised learning under sparse settings and good for general prediction. Despite its effectiveness, FMs can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive.

Technology

Attentional Factorization Machine (AFM) is a novel model which improves FM by discriminating the importance of different feature interactions and learns the importance of each feature interaction from data via a neural attention network. More importantly, the importance of a feature interaction is automatically learned from data without any human domain knowledge.

Stage of Development

Analytical and laboratory studies to validate analytical predictions   

Applications

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.

Advantages

  1. The importance of a feature interaction is automatically learned from data without any human domain knowledge.
  2. AFM consistently outperforms the state-of-the-art deep learning methods with a much simpler structure and fewer model parameters, leading to better performance.
  3. AFM provides insights into which feature interactions contribute more to the prediction. This greatly enhances the interpretability and transparency of FM, allowing practitioners to perform deeper analysis of its behaviour.

Status

Patent pending. Available for licensing.

Inventor

Prof Chua Tat Seng

NExT Centre for Extreme Search

E-mail: dcscts@comp.nus.edu.sg

Website: http://www.nextcenter.org/

Contact

Asha Srinivasan

Phone: +65-65161671

E-mail: asha.srinivasan@nus.edu.sg

ILO Ref No: 2017-075