Monday Poster Session: Doctoral Symposium + Workshops

Date: Monday September 22

Doctoral Symposium Papers

• SPOT #1Adding Value to Low-Resource Industrial Recommender Systems
  by Cornelia M Klopper

  This research proposes a modular, resource-aware framework for industrial recommender systems that enables the integration and evaluation of stakeholder values at each stage of the recommendation pipeline. Motivated by the practical constraints of data availability and computational capacity, the framework supports stage-wise optimisation and selective retraining, making it suitable for low-resource environments. Ongoing experiments on open-source and real-world datasets aim to validate the framework’s adaptability, offering a contribution to the design of value-aware and operationally viable recommender systems.

• SPOT #2Addressing Multi-stakeholder Fairness Concerns in Recommender Systems Through Social Choice
  by Amanda Aird

  Fairness in recommender systems has been discussed on the group and individual level with concerns for both providers and consumers. But many current solutions to improving fairness in recommender systems can only address one fairness concern or have limited definitions of fairness. My research revolves around improving fairness in recommender systems with an approach that addresses multiple and complex fairness concerns. I use SCRUF-D (Social Choice for Recommendation Under Fairness – Dynamic), a multi-agent social choice-based architecture, for reranking recommendations to improve fairness across multiple dimensions. My completed research has evaluated trade-offs between accuracy and fairness when reranking for multiple fairness definitions on the provider side. This includes exploring how different social choice rules and agent allocation mechanisms impact this trade-off. Currently, I am focused on expanding these studies to include individual and consumer-side fairness metrics. My ongoing research aims to evaluate the trade-offs between accuracy and fairness, incorporating consumer-side fairness metrics. Research to handle tensions between different types of fairness and human research to demonstrate the value of SCRUF is being planned.

• SPOT #3Advancing User-Centric Evaluation and Design of Conversational Recommender Systems
  by Michael Müller

  Conversational Recommender Systems (CRS) are rapidly evolving with advancements in large language models (LLMs), enabling richer, more adaptive user interactions. However, existing evaluation practices remain largely system-centric, underestimating nuanced factors like conversational quality, empathy, and real-world user satisfaction. This doctoral research aims to bridge that gap by advancing holistic, user-centric evaluation frameworks for CRS. The work pursues four directions: (1) identifying key drivers of user satisfaction through targeted user studies and dataset analyses; (2) systematically investigating LLMs as annotators and user simulators to support scalable CRS assessment; (3) developing scalable, standardized evaluation protocols that balance objective accuracy with subjective conversational experience; and (4) deriving actionable design guidelines by comparing strategies for preference elicitation and context integration. Ultimately, this research seeks to provide reproducible methods, and evidence-based guidance to foster the development of CRS that genuinely center the user.

• SPOT #4Are Recommender Systems Serving Children? Toward Child-Aware Design and Evaluation
  by Robin Ungruh

  Recommender Systems research continuously improves recommendation strategies to meet the needs of a wide range of users and other stakeholders. However, much of this research centers on the traditional, adult user, often overlooking underrepresented demographics. One such group is children, frequent users of platforms driven by recommender systems. Children differ from adults in preferences and can be particularly vulnerable to certain content, raising questions about the harm recommender systems may pose. This PhD project advocates for child-aware recommender systems: systems that explicitly account for children as part of their users, recognizing their distinct needs, vulnerabilities, and rights. In pursuit of this goal, we investigate how well current recommender systems serve children, auditing algorithmic strategies from two complementary perspectives: The ‘traditional’ perspective focuses on whether recommendations align with children’s preferences. The perspective of ‘non-maleficence’ assesses suitability of content recommended, evaluating whether it respects children’s vulnerabilities to potentially harmful material. To do so, we audit current recommender systems according to both perspectives—not only in the short term, but also in the long term through simulation studies. Beyond auditing, we explore strategies and design directions for making recommender systems more responsible. Outcomes from this work should inform both academic and practitioner communities about the gaps in current systems and lay the groundwork for more equitable, safe, and meaningful recommendations for children.

• SPOT #5Bayesian Perspectives on Offline Evaluation for Recommender Systems
  by Michael Benigni

  Offline evaluation is a fundamental component in the deployment and development of better recommender systems. In recent years, the contextual bandit framework has emerged as a valuable approach for offline and counterfactual evaluation, leading to the increasing interest in estimators based on inverse propensity scoring (IPS), direct methods (DM), and doubly robust (DR) techniques. However, nearly all existing methods rely on frequentist statistics, limiting their ability to capture model uncertainty and reflecting it in evaluation outcomes. This work explores the novel research direction of Bayesian statistics for Off-Policy Evaluation in recommendation tasks, motivated by the need for reliable estimators that are more robust to distribution shift, data sparsity, and model misspecification. Three underexplored research directions are identified in this work: (i) using posterior uncertainty from Bayesian reward models to design adaptive hybrid estimators, (ii) explicitly modeling all components of the OPE problem (contexts, actions, and rewards) using a joint probabilistic framework, and (iii) quantifying epistemic uncertainty over policy value estimates via posterior inference. By leveraging the Bayesian framework, the aim is to improve the reliability, interpretability, and safety of offline evaluation protocols, offering a new perspective on one of the most persistent challenges in recommender systems research. This perspective is especially relevant in data-scarce or high-stakes settings, where understanding uncertainty is essential for trustworthy decision-making.

• SPOT #6Beyond Persuasion: Adaptive Warnings and Balanced Explanations for Informed Decision-Making in Recommender Systems
  by Elaheh Jafari

  As recommender systems become deeply embedded in digital platforms, designing explanations that are ethical, effective, and user-centered is increasingly important. Traditional strategies often prioritize persuasiveness or transparency but neglect user agency and cognitive differences. This research explores alternative explanation formats, warnings that highlight potential drawbacks and balanced pros-and-cons summaries, to support more informed and autonomous decision-making. In the first year, we published a paper discussing ethical considerations in explanation design for recommender systems. We then conducted a systematic review of user perceptions, a study of warning messages in mobile app interfaces, and a controlled e-commerce experiment comparing baseline, warning, and pros-and-cons explanations. Results indicate that layered explanations improve decision satisfaction, reduce cognitive load, and better align with individual traits like decision style and need for cognition. Building on these findings, we propose a multi-level explanation approach that combines upfront warnings with on-demand balanced details, adaptable across domains. Future work will explore personalization strategies, real-time adaptivity, and generalizability to domains such as media, news, and job recommendations. This research aims to inform the design of transparent, fair, and trustworthy explanation interfaces in recommender systems.

• SPOT #7Challenges in Perfume Recommender Systems: Navigating Subjectivity, Context and Sensory Data
  by Elena-Ruxandra Lutan

  Compared to other recommender systems domains, perfume recommendation proves to be highly personalized and more challenging due to the very subjective factors and complex mixture of involved senses. Individual perfume preferences are influenced by subtle elements such as emotional associations, personal memories, and unique biochemistry, making it difficult for users to clearly express their olfactory preferences. This paper provides an insight of significant challenges in perfume recommendations planned to be addressed in the context of my ongoing PhD project. By exploring these areas, I aim to make a meaningful contribution to the ongoing development of perfume recommender systems.

• SPOT #8Fair and Transparent Recommender Systems for Advertisements
  by Dina Zilbershtein

  Recommender systems are central to digital platforms, powering content personalization, user engagement, and revenue generation. In advertising, they operate within a multi-stakeholder environment, bringing together viewers, advertisers, and platform providers with often competing objectives. While such systems enhance targeting precision, their opacity raises concerns around fairness, transparency, and trust. This research, conducted in collaboration with RTL Netherlands, focuses on building fair and transparent recommender systems for advertisements, with particular emphasis on Video-on-Demand (VoD) platforms. I investigate algorithmic interventions and explainability techniques aimed at aligning system behavior with stakeholders’ expectations. By addressing tensions between stakeholders’ objectives and challenges of the ad delivery process, this work contributes to the design of ethically responsible advertising systems that balance commercial goals with accountability and user trust.

• SPOT #9Full-Page Recommender: A Modular Framework for Multi-Carousel Recommendations
  by Jan Kislinger

  Full-page layouts with multiple carousels are widely used in video streaming platforms, yet understudied in recommender systems research. This paper introduces a structured approach to generating such pages by recommending coherent item collections and optimizing their arrangement. We break the problem into subcomponents and propose methods that balance user relevance, diversity, and coherence. We also present an evaluation framework tailored to this setting. We argue that this approach can improve recommendation quality beyond traditional ranked lists.

• SPOT #10Narrative-Driven Itinerary Recommendation: LLM Integration for Immersive Urban Walking
  by Fabio Ferrero

  Sedentary behavior, dubbed the disease of the 21st century, is a ubiquitous force driving chronic illness. Yet, traditional itinerary and Point-of-Interest (POI) Recommender Systems (RSs) lack engaging elements that motivate routine urban walking. This research proposes a novel framework combining narrative-driven storytelling with location-based RSs to promote physical activity and immersive urban exploration. This approach introduces a bidirectional alignment between POI and itinerary recommendations and LLM-generated narratives, transforming routine urban walks into dynamic journeys where contextually relevant stories unfold across city locations. Unlike sequential POI recommendations, this framework embeds location suggestions within contextually relevant narratives of various genres, simultaneously promoting health benefits and deeper city exploration. The research addresses three research questions using a method that builds a structured knowledge base by extracting entities (e.g., POIs, and characters) and semantic links from narrative corpora, enabling semantic alignment between recommended physical locations and story elements. The core aspects of this work are: (i) context-aware itinerary recommendations and personalized story generation, (ii) bidirectional mapping between RSs and story generation, and (iii) systems design bridging user’s needs to promote urban walking as a health activity. Evaluation employs comparative user studies measuring quality and engagement, route-narrative semantic alignment, and narrative analysis to validate the integrated proposed approach.

• SPOT #11Personalized Image Generation for Recommendations Beyond Catalogs
  by Gabriel Alfonso Patron

  Retrieval-based recommender systems are constrained by fixed catalogs, limiting their ability to serve diverse and evolving user preferences. We propose REBECA (REcommendations BEyond CAtalogs), a new class of preference-aware generative models for recommendation that synthesizes images tailored to individual tastes rather than retrieving items. REBECA conditions a diffusion model on users’ feedback (e.g., ratings) to generate personalized image embeddings in CLIP space, which are decoded into images via an adapter-on-adapter architecture that bypasses the need for image captions during training. By leveraging an expressive pre-trained image decoder and a lightweight probabilistic adapter, REBECA enables general-purpose image generation aligned with users’ visual preferences across diverse domains without expensive fine-tuning. We also introduce a new benchmark for personalized generation based on a curated version of the FLICKR-AES dataset, along with two novel personalization metrics tailored to the generative setting. Empirical results show that REBECA produces high-quality, diverse, and preference-aligned outputs, outperforming prompt-based personalization baselines on key personalization and quality metrics. By augmenting traditional retrieval with generative modeling, REBECA opens new opportunities for applications such as content design, personalization-first creative platforms, and preference-aware synthetic media.

• SPOT #12Recommender Systems for Digital Humanities and Archives: Multistakeholder Evaluation, Scholarly Information Needs, and Multimodal Similarity
  by Florian Atzenhofer-Baumgartner

  Recommender systems (RecSys) in digital humanities (DH) and archives face unique challenges, including balancing competing stakeholder values, serving complex scholarly information needs, and modeling multimodal historical artifacts. This paper reports on ongoing research that tackles these issues through three interconnected strands: (1) the development of co-designed multistakeholder evaluation frameworks that move beyond simple engagement metrics to capture diverse priorities among archivists, researchers, and platform owners; (2) a systematic examination of the information behaviors of humanities scholars to inform user models adapted to exploratory, non-linear research; and (3) the creation of multimodal similarity metrics that exploit scholarly markup, material characteristics, and specialized domain knowledge. Validated through Monasterium.net—the world’s largest charter archive—this research contributes novel approaches to value-driven evaluation, scholarly user modeling, and historical document similarity. It provides methodological frameworks to bridge the computer science and DH communities, and to advance multistakeholder RecSys for complex, non-traditional domains.

Gen-AI Workshop Papers

From spot #13 to spot #20

CONSEQUENCES Workshop Papers

From spot #21 to spot #39