Ing. Vojtěch Vančura

Stať ve sborníku

10.1145/3523227.3551482

Katedra aplikované matematiky

Recently, the RS research community has witnessed a surge in popularity for shallow autoencoder-based CF methods. Due to its straightforward implementation and high accuracy on item retrieval metrics, EASE is potentially the most prominent of these models. Despite its accuracy and simplicity, EASE cannot be employed in some real-world recommender system applications due to its inability to scale to huge interaction matrices. In this paper, we proposed ELSA, a scalable shallow autoencoder method for implicit feedback recommenders. ELSA is a scalable autoencoder in which the hidden layer is factorizable into a low-rank plus sparse structure, thereby drastically lowering memory consumption and computation time. We conducted a comprehensive offline experimental section that combined synthetic and several real-world datasets. We also validated our strategy in an online setting by comparing ELSA to baselines in a live recommender system using an A/B test. Experiments demonstrate that ELSA is scalable and has competitive performance. Finally, we demonstrate the explainability of ELSA by illustrating the recovered latent space.

Stať ve sborníku

10.1145/3523227.3551469

Katedra aplikované matematiky

Making recommender systems more transparent and auditable is crucial for the future adoption of these systems. Available tools typically present mostly errors of models aggregated over all test users, which is often insufficient to uncover hidden biases and problems. Moreover, the emphasis is primarily on the accuracy of recommendations but less on other important metrics, such as the diversity of recommended items, the extent of catalog coverage, or the opportunity to discover novel items at bestsellers’ expense. In this work, we propose RepSys, a framework for evaluating recommender systems. Our work offers a set of highly interactive approaches for investigating various scenario recommendations, analyzing a dataset, and evaluating distributions of various metrics that combine visualization techniques with existing offline evaluation methods. RepSys framework is available under an open-source license to other researchers.

Stať ve sborníku

10.1007/978-3-030-86383-8_11

Katedra aplikované matematiky

The recently introduced Embarrasingelly Shallow Autoencoder (EASE) algorithm presents a simple and elegant way to solve the top-N recommendation task. In this paper, we introduce Neural EASE to further improve the performance of this algorithm by incorporating techniques for training modern neural networks. Also, there is a growing interest in the recsys community to utilize variational autoencoders (VAE) for this task. We introduce Focal Loss Variational AutoEncoder (FLVAE), benefiting from multiple non-linear layers without an information bottleneck while not overfitting towards the identity. We show how to learn FLVAE in parallel with Neural EASE and achieve state-of-the-art performance on the MovieLens 20M dataset and competitive results on the Netflix Prize dataset.