On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural Networks
This work addresses the performance gap in collaborative filtering for recommender systems, but it is incremental as it revisits and explains existing findings rather than introducing new methods.
The paper tackled the problem of why neural networks underperform low-rank approximations in pure collaborative filtering tasks, concluding that neural networks' universal approximation capabilities impair latent vector determination, leading to worse performance.
Even in times of deep learning, low-rank approximations by factorizing a matrix into user and item latent factors continue to be a method of choice for collaborative filtering tasks due to their great performance. While deep learning based approaches excel in hybrid recommender tasks where additional features for items, users or even context are available, their flexibility seems to rather impair the performance compared to low-rank approximations for pure collaborative filtering tasks where no additional features are used. Recent works propose hybrid models combining low-rank approximations and traditional deep neural architectures with promising results but fail to explain why neural networks alone are unsuitable for this task. In this work, we revisit the model and intuition behind low-rank approximation to point out its suitability for collaborative filtering tasks. In several experiments we compare the performance and behavior of models based on a deep neural network and low-rank approximation to examine the reasons for the low effectiveness of traditional deep neural networks. We conclude that the universal approximation capabilities of traditional deep neural networks severely impair the determination of suitable latent vectors, leading to a worse performance compared to low-rank approximations.