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

• URL: http://arxiv.org/abs/2505.04518v1
• Date: Wed, 07 May 2025 15:45:04 GMT
• Title: User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
• Abstract summary: We present a temporal explanatory analysis of the UCSD Book Graph dataset scraped from Goodreads.<n>We examine whether the introduction of algorithmic recommendations in 2011 was followed by observable changes in user or recommender system behavior.
• Score: 2.1775979310224827
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem [0.7711381100220001]
  This paper explores the implications of algorithmic pluralism (also known as "middleware" in the governance literature) in which recommendation algorithms are decoupled from platforms.<n>We examine how data portability scenarios produce varying effects on user utility across different recommendation algorithms.
  arXiv  Detail & Related papers  (2026-04-23T14:59:54Z)
• Continual Recommender Systems [47.467562063027195]
  Current tutorials on machine learning do not address recommendation-specific demands.<n>We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches.<n>We then highlight recent efforts to apply continual learning to practical deployment environments.
  arXiv  Detail & Related papers  (2025-07-05T02:20:15Z)
• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions.<n>For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach [11.392605386729699]
  We propose a hidden semi-Markov model to track the change of users' interests.<n>This model allows for capturing the different durations of user stays in a (latent) interest state.<n>We derive an algorithm to estimate the parameters and predict users' actions.
  arXiv  Detail & Related papers  (2024-12-15T09:17:45Z)
• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• Impression-Aware Recommender Systems [53.48892326556546]
  We present a systematic literature review on recommender systems using impressions.<n>We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-based Recommendation in E-Commerce [38.42250061908039]
  In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items. We present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation.
  arXiv  Detail & Related papers  (2020-10-17T17:22:35Z)
• Leveraging Historical Interaction Data for Improving Conversational Recommender System [105.90963882850265]
  We propose a novel pre-training approach to integrate item- and attribute-based preference sequence. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-08-19T03:43:50Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.