Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
In the field of recommender systems, shallow autoencoders have recently gained significant attention. One of the most highly acclaimed shallow autoencoders is EASE, favored for its competitive recommendation accuracy and simultaneous simplicity. However, the poor scalability of EASE (both in time and especially in memory) severely restricts its use in production environments with vast item sets. In this paper, we propose a hyperefficient factorization technique for sparse approximate inversion of the data-Gram matrix used in EASE. The resulting autoencoder, SANSA, is an end-to-end sparse solution with prescribable density and almost arbitrarily low memory requirements — even for training. As such, SANSA allows us to effortlessly scale the concept of EASE to millions of items and beyond.
Code (1)
Tasks
Collaborative FilteringRecommendation SystemsSimilar Papers 제목 키워드 기반
Neuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice
This paper presents a novel recommendation method called neuron-enhanced autoencoder based collaborative filtering (NE-AECF). The method uses an additional neural network to enhance the reconstruction capability of autoe…
Collaborative FilteringScalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of $n$ items. While conventionally a DPP is parameterized by a symmetric kernel matrix, removing this sym…
Point ProcessesAutoRec: Autoencoders Meet Collaborative Filtering
This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec’s compact and efficiently trainable model outperforms stateof-the-art CF techniques (biased matrix factori…
Collaborative FilteringRecommendation SystemsCollaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity
A recent study has shown that diffusion models are well-suited for modeling the generative process of user-item interactions in recommender systems due to their denoising nature. However, existing diffusion model-based r…
Collaborative FilteringComputational EfficiencyDenoisingRecommendation SystemsHybrid Collaborative Recommendation via Semi-AutoEncoder
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Exper…
Collaborative Filtering