Explainable recommendations with nonnegative matrix factorization

Zhang, Xiaoxia

doi:10.1007/s10462-023-10619-9

2023-10-31

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DOI：10.1007/s10462-023-10619-9

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摘要

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Explicable recommendation system is proved to be conducive to improving the persuasiveness of the recommendation system, enabling users to trust the system more and make more intelligent decisions. Nonnegative Matrix Factorization (NMF) produces interpretable solutions for many applications including collaborative filtering as it's nonnegativity. However, the latent features make it difficult to interpret recommendation results to users because we don't know the specific meaning of features that users are interested in and the extent to which the items or users belong to these features. To overcome this difficulty, we develop a novel method called Partially Explainable Nonnegative Matrix Factorization (PE-NMF) by employing explicit data to replace part latent variables of item-feature matrix, by which users can learn more about the features of the items and then to make ideal decisions and recommendations. The objective function of PE-NMF is composed of two parts: one part corresponding to explicit features and the other part is about implicit features. We develop an iterative method to minimize the objective function and derive the iterative update rules, with which the objective function can be proved to be decreasing. Finally, the experiments are executed on Yelp, Amazon and Dianping datasets, and the experimental results demonstrate PE-NMF keeps a high prediction performance on both rating prediction and top-N recommendation that compare to fully explainable nonnegative matrix factorization (FE-NMF), which is obtained by using explicit opinions instead of item-feature matrix. Also PE-NMF holds almost the same recommendation ability as NMF.

Keyword:

Nonnegative matrix factorization

Partially explainable nonnegative matrix factorization

Fully explainable nonnegative matrix factorization

Recommendation explnation