---
title: "Practical Linear Models for Large-Scale One-Class Collaborative Filtering"
authors:
  - "Suvash Sedhain"
  - "Hung Hai Bui"
  - "Jaya Kawale"
  - "Nikos Vlassis"
  - "Branislav Kveton"
  - "Aditya Krishna Menon"
  - "Trung Bui"
  - "Scott Sanner"
doi: ""
url: "https://ijcai.org/proceedings/2016/542"
pdf: "https://ijcai.org/proceedings/2016/0542.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3854-3859"
track: ""
keywords:
---

# Practical Linear Models for Large-Scale One-Class Collaborative Filtering

Suvash Sedhain, Hung Hai Bui, Jaya Kawale, Nikos Vlassis, Branislav Kveton, Aditya Krishna Menon, Trung Bui, Scott Sanner

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3854-3859.

## Abstract

Collaborative filtering has emerged as the de facto approach to personalized recommendation problems. However, a scenario that has proven difficult in practice is the one-class collaborative filtering case (OC-CF), where one has examples of items that a user prefers, but no examples of items they do not prefer. In such cases, it is desirable to have recommendation algorithms that are personalized, learning-based, and highly scalable. Existing linear recommenders for OC-CF achieve good performance in benchmarking tasks, but they involve solving a large number of a regression subproblems, limiting their applicability to large-scale problems. We show that it is possible to scale up linear recommenders to big data by learning an OC-CF model in a randomized low-dimensional embedding of the user-item interaction matrix. Our algorithm, Linear-FLow, achieves state-of-the-art performance in a comprehensive set of experiments on standard benchmarks as well as real data.