Collaborative Filtering with Side Information: a Gaussian Process Perspective

2016/05/23 by Hyunjik Kim, Xiaoyu Lu, Kim, Hyunjik +5 · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.1605.07025

openalex publication_date 2016/05/23 · openalex created_date 2017/06/15 · openalex updated_date 2026/07/28

Abstract

We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at our model, the Tucker Gaussian Process (TGP). Consequently, TGP generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems. Moreover we show that it is a novel model for regression, especially well-suited to grid-structured data and problems where the dependence on covariates is close to being separable.

Citations

• Some Mathematical Notes on Three-Mode Factor Analysis
• Inference from Iterative Simulation Using Multiple Sequences

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