User and Recommender Behavior Over Time: Contextualizing Activity Effectiveness Diversity and Fairness in Book Recommendation. Vaez Barenji, S., Parajuli, S., & Ekstrand, M. D. In Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, of UMAP Adjunct '25, pages 280–287, New York, NY, USA, June, 2025. Association for Computing Machinery. ![User and Recommender Behavior Over Time: Contextualizing Activity Effectiveness Diversity and Fairness in Book Recommendation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{vaez_barenji_user_2025,
address = {New York, NY, USA},
series = {{UMAP} {Adjunct} '25},
title = {User and {Recommender} {Behavior} {Over} {Time}: {Contextualizing} {Activity} {Effectiveness} {Diversity} and {Fairness} in {Book} {Recommendation}},
isbn = {979-8-4007-1399-6},
shorttitle = {User and {Recommender} {Behavior} {Over} {Time}},
url = {https://dl.acm.org/doi/10.1145/3708319.3733710},
doi = {10.1145/3708319.3733710},
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.},
urldate = {2025-06-22},
booktitle = {Adjunct {Proceedings} of the 33rd {ACM} {Conference} on {User} {Modeling}, {Adaptation} and {Personalization}},
publisher = {Association for Computing Machinery},
author = {Vaez Barenji, Samira and Parajuli, Sushobhan and Ekstrand, Michael D.},
month = jun,
year = {2025},
pages = {280--287},
}
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