More Like this

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

   https://doi.org/10.1145/3708319.3733710  

   Vaez_Barenji, Samira; Parajuli, Sushobhan; Ekstrand, Michael D (June 2025, ACM)

   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. 

   more » « less
2. Reproduction code for "User and Recommender Behavior Over Time" at FairUMAP 2025

   https://doi.org/10.5281/zenodo.15333265  

   Vaez_Barenji, Samira; Parajuli, Sushobhan; Ekstrand, Michael (January 2025, Zenodo)

   This repository contains the reproduction code for "User and Recommender Behavior Over Time" paper at FairUMAP 2025: Samira Vaez Barenji, Sushobhan Parajuli, Michael D. Ekstrand. 2025. User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation. In Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct ’25). ACM. DOI: 10.1145/3708319.3733710. arXiv: 2505.04518 [cs.IR]. 

   more » « less
3. Sequential Recommendation with User Memory Networks

   https://doi.org/10.1145/3159652.3159668  

   Chen, Xu; Xu, Hongteng; Zhang, Yongfeng; Tang, Jiaxi; Cao, Yixin; Qin, Zheng; Zha, Hongyuan (January 2018, Web Search and Data Mining)

   User preferences are usually dynamic in real-world recommender systems, and a user’s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms – including both shallow and deep approaches – usually embed a user’s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user’s historical records and future interests. In this paper, we aim to express, store, and manipulate users’ historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users’ historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users’ sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users’ future actions are affected by previous behaviors. 

   more » « less
4. Fairness in Recommendation: Foundations, Methods and Applications

   https://doi.org/10.1145/3610302  

   Li, Yunqi; Chen, Hanxiong; Xu, Shuyuan; Ge, Yingqiang; Tan, Juntao; Liu, Shuchang; Zhang, Yongfeng (July 2023, ACM Transactions on Intelligent Systems and Technology)

   As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond. 

   more » « less
5. Dynamic Tensor Recommender Systems

   Zhang, Yanqing; Bi, Xuan; Tang, Niansheng; Qu, Annie (February 2021, Journal of machine learning research)

   Anandkumar, Animashree (Ed.)

   Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying coefficient model for temporal tensor factorization through a polynomial spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the spline coefficient estimator. The proposed method is applied to simulations, IRI marketing data and Last.fm data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting. 

   more » « less