| Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS) | 0 | 0.34 | 2022 |
| Semantic Interpretation of Top-N Recommendations | 2 | 0.36 | 2022 |
| Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization | 2 | 0.36 | 2021 |
| How to put users in control of their data in federated top-N recommendation with learning to rank | 0 | 0.34 | 2021 |
| A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images | 5 | 0.39 | 2021 |
| How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework - Data Processing, Model Selection, and Performance Evaluation. | 0 | 0.34 | 2021 |
| Sparse Embeddings for Recommender Systems with Knowledge Graphs. | 0 | 0.34 | 2021 |
| Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation | 7 | 0.42 | 2021 |
| A Flexible Framework For Evaluating User And Item Fairness In Recommender Systems | 3 | 0.38 | 2021 |
| Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to Applications | 0 | 0.34 | 2021 |
| MSAP - Multi-Step Adversarial Perturbations on Recommender Systems Embeddings. | 0 | 0.34 | 2021 |
| V-Elliot: Design, Evaluate and Tune Visual Recommender Systems | 1 | 0.35 | 2021 |
| Sparse Feature Factorization for Recommender Systems with Knowledge Graphs | 3 | 0.36 | 2021 |
| Third Knowledge-aware and Conversational Recommender Systems Workshop (KaRS) | 0 | 0.34 | 2021 |
| Adherence and Constancy in LIME-RS Explanations for Recommendation (Long paper). | 0 | 0.34 | 2021 |
| A Formal Analysis of Recommendation Quality of Adversarially-trained Recommenders | 0 | 0.34 | 2021 |
| The 2021 RecSys Challenge Dataset: Fairness is not optional | 0 | 0.34 | 2021 |
| RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline | 0 | 0.34 | 2021 |
| The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation Learning | 0 | 0.34 | 2021 |
| Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario | 0 | 0.34 | 2020 |
| Prioritized Multi-Criteria Federated Learning | 1 | 0.35 | 2020 |
| Proceedings of the Second Workshop on Knowledge-aware and Conversational Recommender Systems, co-located with 28th ACM International Conference on Information and Knowledge Management, KaRS@CIKM 2019, Beijing, China, November 7, 2019. | 0 | 0.34 | 2020 |
| Assessing Perceptual and Recommendation Mutation of Adversarially-Poisoned Visual Recommenders (short paper). | 0 | 0.34 | 2020 |
| Adversarial Learning for Recommendation: Applications for Security and Generative Tasks — Concept to Code | 0 | 0.34 | 2020 |
| SAShA - Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs. | 4 | 0.38 | 2020 |
| RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter’s Home Timeline | 0 | 0.34 | 2020 |
| Combining RDF and SPARQL with CP-theories to reason about preferences in a Linked Data setting | 2 | 0.36 | 2020 |
| Knowledge-enhanced Shilling Attacks for Recommendation. | 0 | 0.34 | 2020 |
| On the discriminative power of hyper-parameters in cross-validation and how to choose them | 5 | 0.43 | 2019 |
| Recommender Systems Fairness Evaluation via Generalized Cross Entropy. | 1 | 0.34 | 2019 |
| Towards Effective Device-Aware Federated Learning. | 6 | 0.43 | 2019 |
| Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization | 8 | 0.42 | 2019 |
| 2nd Workshop on Knowledge-aware and Conversational Recommender Systems - KaRS | 3 | 0.37 | 2019 |
| Local Popularity and Time in top-N Recommendation. | 8 | 0.44 | 2019 |
| Semantic interpretability of latent factors for recommendation. | 0 | 0.34 | 2019 |
| How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs | 10 | 0.44 | 2019 |
| Anna - A Virtual Assistant to Interact with Puglia Digital Library. | 0 | 0.34 | 2019 |
| Proceedings of the Workshop on Knowledge-aware and Conversational Recommender Systems 2018 co-located with 12th ACM Conference on Recommender Systems, KaRS@RecSys 2018, Vancouver, Canada, October 7, 2018. | 0 | 0.34 | 2018 |
| Knowledge-aware and conversational recommender systems. | 4 | 0.39 | 2018 |
| Moving from Item Rating to Features Relevance in Top-N Recommendation. | 0 | 0.34 | 2018 |
| The importance of being dissimilar in Recommendation. | 2 | 0.37 | 2018 |
| Time and Local Popularity in top-N Recommendation. | 0 | 0.34 | 2018 |
| Ontology-based Linked Data Summarization in Semantics-aware Recommender Systems. | 0 | 0.34 | 2018 |
| Etytree: A Graphical and Interactive Etymology Dictionary based on Wiktionary. | 0 | 0.34 | 2017 |
| On the Role of Time and Sessions in Diversifying Recommendation Results. | 0 | 0.34 | 2017 |
| Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios. | 4 | 0.41 | 2017 |
| Feature Factorization for Top-N Recommendation: From Item Rating to Features Relevance. | 0 | 0.34 | 2017 |
| Querying deep web data sources as linked data | 0 | 0.34 | 2017 |
| An Analysis on Time- and Session-aware Diversification in Recommender Systems. | 5 | 0.40 | 2017 |
| Exposing Open Street Map in the Linked Data Cloud. | 5 | 0.53 | 2016 |
