| 1 | GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning | 4.0 | 22 | Citations (PDF) |
| 2 | Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation | 4.0 | 2 | Citations (PDF) |
| 3 | Enhancing recommender systems with provider fairness through preference distribution awareness | 5.6 | 3 | Citations (PDF) |
| 4 | GNNFairViz: Visual Analysis for Graph Neural Network Fairness | 2.8 | 4 | Citations (PDF) |
| 5 | ACM Conference on Hypertext and INTR/HT Summer School, September 2025, Chicago, USA | 0.2 | 0 | Citations (PDF) |
| 6 | Accuracy and beyond-accuracy perspectives of controllable multi-objective recommender systems | 6.0 | 8 | Citations (PDF) |
| 7 | Report on the 1st International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) at ECIR 2024 | 0.1 | 0 | Citations (PDF) |
| 8 | Toward a Responsible Fairness Analysis: From Binary to Multiclass and Multigroup Assessment in Graph Neural Network-Based User Modeling Tasks | 2.5 | 6 | Citations (PDF) |
| 9 | Report on the 5th International Workshop on Algorithmic Bias in Search and Recommendation (BIAS 2024) at SIGIR 2024 | 0.1 | 0 | Citations (PDF) |
| 10 | Reinforcement recommendation reasoning through knowledge graphs for explanation path quality | 7.0 | 32 | Citations (PDF) |
| 11 | Practical perspectives of consumer fairness in recommendation | 6.0 | 30 | Citations (PDF) |
| 12 | Bias characterization, assessment, and mitigation in location-based recommender systems | 2.7 | 10 | Citations (PDF) |
| 13 | A Robust Reputation-Based Group Ranking System and Its Resistance to Bribery | 3.1 | 12 | Citations (PDF) |
| 14 | Enabling cross-continent provider fairness in educational recommender systems | 5.6 | 42 | Citations (PDF) |
| 15 | Provider fairness across continents in collaborative recommender systems | 6.0 | 36 | Citations (PDF) |
| 16 | Robust reputation independence in ranking systems for multiple sensitive attributes | 1.8 | 8 | Citations (PDF) |
| 17 | XRecSys: A framework for path reasoning quality in explainable recommendation | 1.1 | 4 | Citations (PDF) |
| 18 | Fair performance-based user recommendation in eCoaching systems | 1.8 | 5 | Citations (PDF) |
| 19 | ACM UMAP 2022 report | 0.2 | 0 | Citations (PDF) |
| 20 | 33rd ACM conference on hypertext and social media | 0.2 | 0 | Citations (PDF) |
| 21 | Connecting user and item perspectives in popularity debiasing for collaborative recommendation | 6.0 | 116 | Citations (PDF) |
| 22 | Interplay between upsampling and regularization for provider fairness in recommender systems | 1.8 | 52 | Citations (PDF) |
| 23 | Integrating Collaboration and Leadership in Conversational Group Recommender Systems | 4.7 | 23 | Citations (PDF) |
| 24 | Toward a Complete Data Valuation Process. Challenges of Personal Data | 1.6 | 3 | Citations (PDF) |
| 25 | Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations | 3.1 | 40 | Citations (PDF) |
| 26 | On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms | 6.0 | 42 | Citations (PDF) |
| 27 | Integrating a cognitive assistant within a critique-based recommender system | 1.7 | 15 | Citations (PDF) |
| 28 | Characterizing user behavior in journey planning | 2.2 | 11 | Citations (PDF) |
| 29 | Report on the international workshop on algorithmic bias in search and recommendation (Bias 2020) | 0.1 | 1 | Citations (PDF) |
| 30 | Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations | 3.5 | 13 | Citations (PDF) |
| 31 | Modeling real-time data and contextual information from workouts in eCoaching platforms to predict users’ sharing behavior on Facebook | 1.8 | 1 | Citations (PDF) |
| 32 | Recommender System Lets Coaches Identify and Help Athletes Who Begin Losing Motivation | 0.7 | 14 | Citations (PDF) |
| 33 | A multi-biometric system for continuous student authentication in e-learning platforms | 3.0 | 70 | Citations (PDF) |
| 34 | Semantics-aware content-based recommender systems: Design and architecture guidelines | 5.8 | 38 | Citations (PDF) |
| 35 | An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes | 1.3 | 26 | Citations (PDF) |
| 36 | Using social media to characterize urban mobility patterns: State-of-the-art survey and case-study | 1.9 | 36 | Citations (PDF) |
| 37 | Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios | 6.4 | 31 | Citations (PDF) |
| 38 | The role of social interaction on users motivation to exercise: A persuasive web framework to enhance the self-management of a healthy lifestyle | 2.8 | 15 | Citations (PDF) |
| 39 | Report on the Workshop on Social Media for Personalization And Search (SoMePeAS) | 0.1 | 0 | Citations (PDF) |
| 40 | A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system | 2.5 | 22 | Citations (PDF) |
| 41 | Using neural word embeddings to model user behavior and detect user segments | 7.0 | 24 | Citations (PDF) |
| 42 | Binary sieves: Toward a semantic approach to user segmentation for behavioral targeting | 5.6 | 10 | Citations (PDF) |
| 43 | Influence of Rating Prediction on Group Recommendation's Accuracy | 2.0 | 12 | Citations (PDF) |
| 44 | Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering | 5.6 | 50 | Citations (PDF) |
| 45 | ART: group recommendation approaches for automatically detected groups | 2.1 | 28 | Citations (PDF) |
| 46 | Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system | 5.2 | 13 | Citations (PDF) |
| 47 | The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation | 2.5 | 37 | Citations (PDF) |
| 48 | A Proactive Time-frame Convolution Vector (TFCV) Technique to Detect Frauds Attempts in e-Commerce Transactions | 0.1 | 0 | Citations (PDF) |
| 49 | Ensuring provider fairness in recommender systems across coarse- and fine-grained groups | 3.6 | 0 | Citations (PDF) |
| 50 | Recommender systems and sustainability: a dual perspective | 9.5 | 2 | Citations (PDF) |
| 51 | Graph Augmentation for Intersectional Unfairness Mitigation: A Study across Dataset Scales and Interaction Densities 0, , | | 0 | Citations (PDF) |
| 52 | Statistical Filtering for Fair Item Ranking | 4.7 | 0 | Citations (PDF) |
| 53 | The ACM Hypertext 2025 Conference Report | 0.2 | 0 | Citations (PDF) |
