Preprint
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
07 May 2025
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.
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Details
- Title
- User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
- Creators
- Samira Vaez BarenjiSushobhan ParajuliMichael D Ekstrand
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991022052320104721