A recommender network perspective on the informational value of critics and crowds

TL;DR

This paper compares critic and amateur ratings in a wine recommender system, showing critics are more consistent and influential, and combining both improves recommendations, with a novel network approach for analysis.

Contribution

Introduces a new dataset and a network-based framework to analyze the influence of critics and amateurs in recommender systems, revealing their relative consistency and influence.

Findings

Critics are more consistent than amateurs.

Combining critic and amateur advice improves recommendations.

Network analysis identifies influential critics and information flow.

Abstract

How do the ratings of critics and amateurs compare and how should they be combined? Previous research has produced mixed results about the first question, while the second remains unanswered. We have created a new, unique dataset, with wine ratings from critics and amateurs, and simulated a recommender system using the k-nearest-neighbor algorithm. We then formalized the advice seeking network spanned by that algorithm and studied people's relative influence. We find that critics are more consistent than amateurs, and thus their advice is more predictive than advice from amateurs. Getting advice from both groups can further boost performance. Our network theoretic approach allows us to identify influential critics, talented amateurs, and the information flow between groups. Our results provide evidence about the informational function of critics, while our framework is broadly applicable and can be leveraged to devise good decision strategies and more transparent recommender systems.