Conference Proceedings

MMF: Attribute Interpretable Collaborative Filtering

Y Su, SM Erfani, R Zhang

2019 International Joint Conference on Neural Networks (IJCNN) | IEEE | Published : 2019


Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix Factorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present in different items. In the model, predicted item ratings are regarded as weighted aggregations of att..

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Awarded by China Scholarship Council (CSC)

Awarded by Australian Research Council (ARC)

Funding Acknowledgements

This work is supported by China Scholarship Council (CSC) under the Grant CSC #201808240005, and Australian Research Council (ARC) Discovery Project DP180102050.