Curse of “Low” Dimensionality in Recommender Systems

“Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in recommender systems are partly due to low-dimensionality of user and item embeddings, particularly when dot-product models, such as matrix factorization, are used.”

“Although exhaustive tuning of hyperparameters for model sizes (e.g., the number of hidden layers and the size of each layer) is unrealistic due to the experimental burden, the dimensionality of user and item embeddings is rather unnoticed compared with other hyperparameters, such as learning rates and regularization weights.”

In the convention of machine learning, we can often avoid model overfitting by using low-dimensional models. However, such models would suffer from potential long-term negative effects, namely, overfitting toward popularity bias. Consequently, low-dimensionality leads to nondiverse, unfair recommendation results and thus insufficient data collection for producing models that can properly delineate users’ individual tastes.

When developers evaluate and select models based only on ranking quality, a reasonable choice may be to use a low-dimensional model due to the space cost efficiency. However, such a model can lead to extremely nondiverse and unfair recommendation results. Even when developers select models based on both ranking quality and diversity, the versatility of models is severely limited if the dimensionality parameter is tuned for a narrow range of values owing to computational resource constraints.”