Logo image
Back
User and Recommender Behavior Over Time: Contextualizing Activity Effectiveness Diversity and Fairness in Book Recommendation
Conference proceeding   Open access

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

Samira Vaez Barenji, Sushobhan Parajuli and Michael D. Ekstrand
Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp 280-287
16 Jun 2025
DOI: https://doi.org/10.1145/3708319.3733710
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.1145/3708319.3733710View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0,  Open

Abstract

Computing methodologies Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Machine learning approaches Computing methodologies -- Machine learning -- Machine learning approaches -- Factorization methods Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social recommendation Information systems Information systems -- Information retrieval Information systems -- Information retrieval -- Retrieval tasks and goals Information systems -- Information retrieval -- Retrieval tasks and goals -- Document filtering Information systems -- Information retrieval -- Retrieval tasks and goals -- Information extraction Information systems -- Information retrieval -- Retrieval tasks and goals -- Recommender systems Information systems -- Information systems applications Information systems -- Information systems applications -- Data mining
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.

Metrics

14 Record Views

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Logo image