Deconvolving Feedback Loops in Recommender Systems

TL;DR

This paper presents a method to identify and deconvolve feedback loops in recommender systems, enabling recovery of intrinsic user preferences from a single ratings snapshot without temporal data.

Contribution

It introduces a tractable approach to deconvolve feedback effects and a metric to measure recommender influence on user-item ratings.

Findings

Successfully identifies the extent of feedback influence in datasets.

Ranks frequently recommended items based on influence metrics.

Distinguishes between recommended items and intrinsic user preferences.

Abstract

Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recommender system, and these loops iteratively influence the collaborative filtering algorithm's predictions over time. We investigate whether it is possible to identify items affected by these feedback loops. We state sufficient assumptions to deconvolve the feedback loops while keeping the inverse solution tractable. We furthermore develop a metric to unravel the recommender system's influence on the entire user-item rating matrix. We use this metric on synthetic and real-world datasets to (1) identify the extent to which the recommender system affects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a user's rated item was recommended or an intrinsic preference. Our results indicate that it is possible to recover the ratings matrix of intrinsic user preferences using a single snapshot of the ratings matrix without any temporal information.