On the Effectiveness of Linear Models for One-Class Collaborative Filtering

Abstract

In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks, we argue that it is hindered by two limitations: first, it does not produce user-personalised predictions, which hampers recommendation performance; second, it involves solving a constrained optimisation problem, which impedes fast training. In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacrificing any of SLIM’s strengths. At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and significantly outperforms all existing methods on a range of datasets. Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classification models.

In proceedings of the Thirtieth Conference on Advancement of Artificial Intelligence (AAAI 2016), Phoenix, 2016.

@inproceedings{SedhainEtAl-AAAI16,
author = "Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Darius Braziunas",
title = "On the Effectiveness of Linear Models for One-Class Collaborative Filtering",
booktitle = "Proceedings of the Thirtieth AAAI Conference",
address = "Phoenix, AZ",
year = "2016"}


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