Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA

TL;DR

This paper introduces a fast, adaptive PCA-based model for collaborative filtering that significantly improves computational efficiency while maintaining high accuracy, especially on large-scale datasets like MovieLens.

Contribution

The paper presents a novel adaptive PCA framework with an automatic termination mechanism, enabling efficient and accurate matrix factorization for recommender systems.

Findings

Adaptive PCA is up to 6.7X faster than traditional SVD methods.

The proposed CF approach achieves over 10X speedup on large datasets.

It maintains comparable or better accuracy than existing methods.

Abstract

A model-based collaborative filtering (CF) approach utilizing fast adaptive randomized singular value decomposition (SVD) is proposed for the matrix completion problem in recommender system. Firstly, a fast adaptive PCA frameworkis presented which combines the fixed-precision randomized matrix factorization algorithm [1] and accelerating skills for handling large sparse data. Then, a novel termination mechanism for the adaptive PCA is proposed to automatically determine a number of latent factors for achieving the near optimal prediction accuracy during the subsequent model-based CF. The resulted CF approach has good accuracy while inheriting high runtime efficiency. Experiments on real data show that, the proposed adaptive PCA is up to 2.7X and 6.7X faster than the original fixed-precision SVD approach [1] and svds in Matlab repsectively, while preserving accuracy. The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy. It also owns the advantage of parameter free. Compared with the deep-learning-based CF approach, the proposed approach is much more computationally efficient, with just marginal performance loss.