Online ranking systems face the challenge of incorporating temporal information while maintaining ethical principles that avoid user profiling. Traditional approaches either ignore temporal dynamics entirely or rely on user reputation schemes that raise privacy and bias concerns. In this work, we propose T-UARS (Temporal User-Agnostic Ranking System), a methodological framework that integrates temporal awareness into ranking systems without assigning scores to users. Our approach uses exponential decay weighting and adaptive change detection to distinguish between anomalous ratings and genuine temporal patterns in item evaluations. We evaluate T-UARS on four datasets with varying temporal characteristics, showing that our framework maintains robust performance across domains, adapts to temporal signals in 10–44% of items, and remains resistant to manipulation while being computationally efficient. Our results show that temporal awareness and ethical ranking principles can be successfully combined, providing a foundation for privacy-preserving ranking systems that adapt to changing information landscapes. Source code: https://tinyurl.com/bp87nk8f.

Online item ranking systems are crucial for digital marketplaces, directly influencing user experience and vendor revenue. Traditional reputation-based ranking systems weight ratings according to user reputation scores. They have proven effective against manipulation but raise significant ethical concerns regarding user discrimination and privacy, and may raise concerns under emerging regulatory frameworks in certain application contexts. While a user-agnostic ranking system was recently introduced as an alternative approach that uses statistical filtering instead of user reputation scores, its theoretical foundations and resistance to bribing strategies remained unexplored. In this article, we provide the first comprehensive theoretical analysis of user-agnostic ranking system’s robustness properties. We establish formal bounds on bribing resistance by proving three key properties: strategy composition conditions, profitability constraints, and statistical validity requirements. Our theoretical framework demonstrates that profitable bribing strategies in this class of system must satisfy strict statistical conditions, making manipulation more difficult than in reputation-based systems. Experimental evaluation on three real-world datasets confirms our theoretical findings, showing that user-agnostic ranking systems can achieve superior bribing resistance while maintaining comparable effectiveness and efficiency.

Designing search and recommendation models that are both efficient and effective has long been a central objective for both industry professionals and academic researchers. Yet, growing evidence highlights how models trained on historical data can reinforce pre-existing biases, potentially leading to harmful outcomes. Addressing these challenges by defining, evaluating, and mitigating bias across development workflows is a crucial step toward the responsible deployment of search and recommendation models in practice. The BIAS 2025 workshop seeks to gather innovative research and foster a shared space for dialogue among researchers and practitioners committed to advancing this fundamental direction. Workshop website: https://biasinrecsys.github.io/sigir2025/.

Autism Spectrum Disorder (ASD) affects sensory perception, making spatial exploration difficult. Recommender systems can assist ASD users by suggesting Points of Interest (POIs) aligned with their sensory preferences. However, demographic constraints, difficulties in engaging ASD users, and the complexity of obtaining sensory data position POI recommendation for ASD people as a low-resource problem. In this paper, we identify key challenges in developing such systems and present our ongoing efforts. Using a local ASD center as a use case, we are developing a structured user involvement protocol. From the limited data, we are deriving knowledge graphs (KGs) to model preferences and sensory aspects. We are then exploring KG-based techniques to generate paths from users to POIs to suggest. With psychologists, we are refining the paths structure to match varying complexity levels and translate them into natural language accessible for people with ASD.

Protecting user privacy in recommender systems is crucial for fostering trust in marketplaces. In this paper, we propose a privacy-preserving framework that integrates public seller rankings into personalized recommendations without exposing sensitive user preferences. By utilizing ''seller representative users'' (encoding seller item rankings) and a novel recommendation mechanism, the framework preserves privacy while ensuring robust ranking accuracy. Our approach is validated on multiple use cases extracted from real-world datasets, showing its effectiveness across varying marketplace configurations. This framework is suited for real-world applications, such as e-commerce platforms, where it can enhance user trust, protect sensitive data, and improve engagement by transparently balancing personalization and privacy.

Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly related to biased recommendations. In this article, we devised a novel algorithm that leverages counterfactuality methods to discover user unfairness explanations in the form of user-item interactions. In our counterfactual framework, interactions are represented as edges in a bipartite graph, with users and items as nodes. Our bipartite graph explainer perturbs the topological structure to find an altered version that minimizes the disparity in utility between the protected and unprotected demographic groups. Experiments on four real-world graphs coming from various domains showed that our method can systematically explain user unfairness on three state-of-the-art GNN-based recommendation models. Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations. The source code and the preprocessed data sets are available at https://github.com/jackmedda/RS-BGExplainer .

As education adopts digital platforms, the vast amount of information from various sources, such as learning management systems and learning object repositories, presents challenges in navigation and elaboration. Traditional interfaces involve a steep learning curve, limited user accessibility, and lack flexibility. Language models alone cannot address these issues as they do not have access to structured information specific to the educational organization. In this paper, we propose EDGE (EDucational knowledge Graph Explorer), a natural language interface that uses knowledge graphs to organize educational information. EDGE translates natural language requests into queries and converts the results back into natural language responses. We show EDGE's versatility using knowledge graphs built from public datasets, providing example interactions of different stakeholders. Demo video: https://u.garr.it/eYq63.

Current recommendation methods based on knowledge graphs rely on entity and relation representations for several steps along the pipeline, with knowledge completion and path reasoning being the most influential. Despite their similarities, the most effective representation methods for these steps differ, leading to inefficiencies, limited representativeness, and reduced interpretability. In this paper, we introduce KGGLM, a decoder-only Transformer model designed for generalizable knowledge representation learning to support recommendation. The model is trained on generic paths sampled from the knowledge graph to capture foundational patterns, and then fine-tuned on paths specific of the downstream step (knowledge completion and path reasoning in our case). Experiments on ML1M and LFM1M show that KGGLM beats twenty-two baselines in effectiveness under both knowledge completion and recommendation. Source code and pre-processed data sets are available at https://github.com/mirkomarras/kgglm.

Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users’ preferences from user-item networks. Despite emerging regulations addressing fairness of automated systems, unfairness issues in graph collaborative filtering remain underexplored, especially from the consumer’s perspective. Despite numerous contributions on consumer unfairness, only a few of these works have delved into GNNs. A notable gap exists in the formalization of the latest mitigation algorithms, as well as in their effectiveness and reliability on cutting-edge models. This paper serves as a solid response to recent research highlighting unfairness issues in graph collaborative filtering by reproducing one of the latest mitigation methods. The reproduced technique adjusts the system fairness level by learning a fair graph augmentation. Under an experimental setup based on 11 GNNs, 5 non-GNN models, and 5 real-world networks across diverse domains, our investigation reveals that fair graph augmentation is consistently effective on high-utility models and large datasets. Experiments on the transferability of the fair augmented graph open new issues for future recommendation studies. Source code: https://github.com/jackmedda/FA4GCF.

Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead to models that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing, and mitigating these biases throughout experimental pipelines is a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. This workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. More information about the workshop is available at https://biasinrecsys.github.io/sigir2024/

Ranking systems are instrumental in shaping user experiences by determining the relevance and order of presented items. However, current approaches, particularly those revolving around user-centric reputation scoring, raise ethical concerns associated with scoring individuals. To counter such issues, in this paper, we introduce a novel item ranking system approach that strategically transitions its emphasis from scoring users to calculating item rankings relying exclusively on items' ratings information, to achieve the same objective. Experiments on three datasets show that our approach achieves higher effectiveness and efficiency than state-of-the-art baselines. Furthermore, the resulting rankings are more robust to spam and resistant to bribery, contributing to a novel and ethically sound direction for item ranking systems.

In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or mitigating disparate impacts in recommendation utility. None of them has leveraged explainability techniques to inform unfairness mitigation. In this paper, we propose an approach that relies on counterfactual explanations to augment the set of user-item interactions, such that using them while inferring recommendations leads to fairer outcomes. Modeling user-item interactions as a bipartite graph, our approach augments the latter by identifying new user-item edges that not only can explain the original unfairness by design, but can also mitigate it. Experiments on two public data sets show that our approach effectively leads to a better trade-off between fairness and recommendation utility compared with state-of-the-art mitigation procedures. We further analyze the characteristics of added edges to highlight key unfairness patterns. Source code available at https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023.

The goal of this tutorial is to present the RecSys community with recent advances on explainable recommender systems with knowledge graphs. We will first introduce conceptual foundations, by surveying the state of the art and describing real-world examples of how knowledge graphs are being integrated into the recommendation pipeline, also for the purpose of providing explanations. This tutorial will continue with a systematic presentation of algorithmic solutions to model, integrate, train, and assess a recommender system with knowledge graphs, with particular attention to the explainability perspective. A practical part will then provide attendees with concrete implementations of recommender systems with knowledge graphs, leveraging open-source tools and public datasets; in this part, tutorial participants will be engaged in the design of explanations accompanying the recommendations and in articulating their impact. We conclude the tutorial by analyzing emerging open issues and future directions. Website: https://explainablerecsys.github.io/recsys2022/.

Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https://github.com/giacoballoccu/explanation-quality-recsys.

Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility.

The impact of ranking systems on humans is an aspect that is getting a lot of attention. In this paper, we consider a class of algorithms, known as reputation-based ranking systems, which rank the items based on a reputation score automatically computed for each user. Recent literature introduced the concept of reputation independence, which considers a sensitive attribute of the users (such as gender or age) and makes the reputation scores independent from that attribute. Here, we show that if we consider a different sensitive attribute w.r.t. a user to introduce independence, reputation scores are still biased. To overcome this issue, we propose an approach to attain equity in the reputation scores computation, independently of any sensitive attribute that characterizes the users.

This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing real-world examples of how bias can impact ranking algorithms from several perspectives (e.g., ethics and system's objectives). Then, we continue with a systematic presentation of techniques to uncover, assess, and mitigate biases along the personalized ranking design process, with a focus on the role of data engineering in each step of the pipeline. Hands-on parts provide attendees with concrete implementations of bias mitigation algorithms, in addition to processes and guidelines on how data is organized and manipulated by these algorithms. The tutorial leverages open-source tools and public datasets, engaging attendees in designing bias countermeasures and in articulating impacts on stakeholders. We finally showcase open issues and future directions in this vibrant and rapidly evolving research area (Website: https://biasinrecsys.github.io/icde2021/).

Educational recommender systems channel most of the research efforts on the effectiveness of the recommended items. While teachers have a central role in online platforms, the impact of recommender systems for teachers in terms of the exposure such systems give to the courses is an under-explored area. In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers. We observe that data is highly imbalanced towards the United States, in terms of offered courses and of interactions. These imbalances are exacerbated by recommender systems, which overexpose the country w.r.t. its representation in the data, thus generating unfairness for teachers outside that country. To introduce equity, we propose an approach that regulates the share of recommendations given to the items produced in a country (visibility) and the position of the items in the recommended list (exposure).

The goal of this tutorial is to provide the WSDM community with recent advances on the assessment and mitigation of data and algorithmic bias in recommender systems. We first introduce conceptual foundations, by presenting the state of the art and describing real-world examples of how bias can impact on recommendation algorithms from several perspectives (e.g., ethical and system objectives). The tutorial continues with a systematic showcase of algorithmic countermeasures to uncover, assess, and reduce bias along the recommendation design process. A practical part then provides attendees with implementations of pre-, in-, and post-processing bias mitigation algorithms, leveraging open-source tools and public datasets; in this part, tutorial participants are engaged in the design of bias countermeasures and in articulating impacts on stakeholders. We conclude the tutorial by analyzing emerging open issues and future directions in this rapidly evolving research area (Website: https://biasinrecsys.github.io/wsdm2021).

By offering courses and resources, learning platforms on the Web have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promoted a significant growth of learning analytics and have opened up new opportunities for supporting and assessing educational experiences. To provide all the stakeholders involved in the educational process with a timely guidance, being able to understand student's behavior and enable models which provide data-driven decisions pertaining to the learning domain is a primary property of online platforms, aiming at maximizing learning outcomes. In this workshop, we focus on collecting new contributions in this emerging area and on providing a common ground for researchers and practitioners (Website: https://mirkomarras.github.io/l2d-wsdm2021).