Slope One Predictors for Online Rating-Based Collaborative Filtering

TL;DR

This paper introduces slope one predictors for collaborative filtering, offering a simple, efficient, and accurate method for predicting user ratings that supports real-time updates and performs well on standard benchmarks.

Contribution

The paper proposes three slope one algorithms with precomputed differences, establishing a new reference scheme for collaborative filtering that balances accuracy and efficiency.

Findings

Competitive results on EachMovie and Movielens datasets.

Supports online queries and dynamic updates.

Simple and efficient implementation.

Abstract

Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f(x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates for real-world systems. The basic slope one scheme is suggested as a new reference scheme for collaborative filtering. By factoring in items that a user liked separately from items that a user disliked, we achieve results competitive with slower memory-based schemes over the standard benchmark EachMovie and Movielens data sets while better fulfilling the desiderata of CF applications.