Analysis of a Collaborative Filter Based on Popularity Amongst Neighbors

TL;DR

This paper provides a theoretical analysis of a popularity-based collaborative filtering algorithm, identifying its performance regimes and validating findings with real-world datasets like MovieLens and Netflix.

Contribution

It introduces a theoretical framework for analyzing the error rates of a popularity-based collaborative filter, filling a gap in understanding its performance.

Findings

In large sample, small degrees of freedom regime, BER approaches zero.

In large sample, large degrees of freedom regime, BER is bounded away from 0 and 1/2.

The algorithm fails with small sample sizes.

Abstract

In this paper, we analyze a collaborative filter that answers the simple question: What is popular amongst your friends? While this basic principle seems to be prevalent in many practical implementations, there does not appear to be much theoretical analysis of its performance. In this paper, we partly fill this gap. While recent works on this topic, such as the low-rank matrix completion literature, consider the probability of error in recovering the entire rating matrix, we consider probability of an error in an individual recommendation (bit error rate (BER)). For a mathematical model introduced in [1],[2], we identify three regimes of operation for our algorithm (named Popularity Amongst Friends (PAF)) in the limit as the matrix size grows to infinity. In a regime characterized by large number of samples and small degrees of freedom (defined precisely for the model in the paper), the asymptotic BER is zero; in a regime characterized by large number of samples and large degrees of freedom, the asymptotic BER is bounded away from 0 and 1/2 (and is identified exactly except for a special case); and in a regime characterized by a small number of samples, the algorithm fails. We also present numerical results for the MovieLens and Netflix datasets. We discuss the empirical performance in light of our theoretical results and compare with an approach based on low-rank matrix completion.