User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation

TL;DR

This study analyzes how user and recommender behaviors in book recommendations evolve over time, focusing on activity, diversity, and fairness, using the UCSD Book Graph dataset and examining the impact of algorithmic recommendations introduced in 2011.

Contribution

It provides a temporal analysis of user and system behavior in book recommendations, highlighting data changes and effects of algorithmic recommendation introduction.

Findings

Behavioral patterns changed after 2011

Diversity and fairness metrics evolved over time

Recommender system activity increased post-2011

Abstract

Data is an essential resource for studying recommender systems. While there has been significant work on improving and evaluating state-of-the-art models and measuring various properties of recommender system outputs, less attention has been given to the data itself, particularly how data has changed over time. Such documentation and analysis provide guidance and context for designing and evaluating recommender systems, particularly for evaluation designs making use of time (e.g., temporal splitting). In this paper, we present a temporal explanatory analysis of the UCSD Book Graph dataset scraped from Goodreads, a social reading and recommendation platform active since 2006. We measure the book interaction data using a set of activity, diversity, and fairness metrics; we then train a set of collaborative filtering algorithms on rolling training windows to observe how the same measures evolve over time in the recommendations. Additionally, we explore whether the introduction of algorithmic recommendations in 2011 was followed by observable changes in user or recommender system behavior.