---
title: "MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering"
authors:
  - "Chao Chen"
  - "Dongsheng Li"
  - "Qin Lv"
  - "Junchi Yan"
  - "Stephen M. Chu"
  - "Li Shang"
doi: ""
url: "https://ijcai.org/proceedings/2016/199"
pdf: "https://ijcai.org/proceedings/2016/0199.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1382-1387"
track: ""
keywords:
---

# MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering

Chao Chen, Dongsheng Li, Qin Lv, Junchi Yan, Stephen M. Chu, Li Shang

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1382-1387.

## Abstract

Matrix approximation (MA) is one of the most popular techniques for collaborative filtering (CF). Most existing MA methods train user/item latent factors based on a user-item rating matrix and then use the global latent factors to model all users/items. However, globally optimized latent factors may not reflect the unique interests shared among only subsets of users/items, without which unique interests of users may not be accurately modelled. As a result, existing MA methods, which cannot capture the uniqueness of different user/item, cannot provide optimal recommendation. In this paper, a mixture probabilistic matrix approximation (MPMA) method is proposed, which unifies globally optimized user/item feature vectors (on the entire rating matrix) and locally optimized user/item feature vectors (on subsets of user/item ratings) to improve recommendation accuracy. More specifically, in MPMA, a method is developed to find both globally and locally optimized user/item feature vectors. Then, a Gaussian mixture model is adopted to combine global predictions and local predictions to produce accurate rating predictions. Experimental study using MovieLens and Netflix datasets demonstrates that MPMA outperforms five state-of-the-art MA based CF methods in recommendation accuracy with good scalability.