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Retrieval-Based Factorization Machines for Human Click Behavior Prediction.

Yu Tang¹, Sheng Wang², Yuancai Huang³

¹Beihang University, Beijing, China.

Computational Intelligence and Neuroscience

|November 28, 2022

Summary

This summary is machine-generated.

This study introduces retrieval-based factorization machines (RFM) for improved click-through rate (CTR) prediction in recommendations. RFM combines global and local user behavior data for more accurate predictions.

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Area of Science:

Computer Science
Machine Learning
Data Mining

Background:

Click-through rate (CTR) prediction is vital for enhancing online recommendation systems.
Existing methods often optimize globally, neglecting valuable local user behavior patterns.

Purpose of the Study:

To propose a novel retrieval-based factorization machines (RFM) approach for accurate CTR prediction.
To integrate global and local information for superior prediction performance.

Main Methods:

Developed a retrieval-based factorization machines (RFM) model.
Employed clustering techniques to partition large datasets for efficient neighbor retrieval.
Combined factorization machines (FM) for global knowledge with neighbor-based local information.

Main Results:

RFM demonstrated superior performance over existing models on three public datasets.
Achieved improved metrics in RMSE, AUC (Area Under the ROC Curve), and accuracy.
The model proved efficient due to a reduced number of parameters.

Conclusions:

The proposed RFM model effectively predicts CTR by leveraging both global and local user behavior.
RFM offers a computationally efficient and accurate solution for CTR prediction in recommender systems.