Elisabeth Lex
YOU?
Author Swipe
View article: Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit
Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit Open
News recommendations are complex, with diversity playing a vital role. So far, existing literature predominantly focuses on specific aspects of news diversity, such as viewpoints. In this paper, we introduce multi-aspect diversification in…
View article: OnSET: Ontology and Semantic Exploration Toolkit
OnSET: Ontology and Semantic Exploration Toolkit Open
Retrieval over knowledge graphs is usually performed using dedicated, complex query languages like SPARQL. We propose a novel system, Ontology and Semantic Exploration Toolkit (OnSET) that allows non-expert users to easily build queries wi…
View article: Differentiable Fuzzy Neural Networks for Recommender Systems
Differentiable Fuzzy Neural Networks for Recommender Systems Open
As recommender systems become increasingly complex, transparency is essential to increase user trust, accountability, and regulatory compliance. Neuro-symbolic approaches that integrate symbolic reasoning with sub-symbolic learning offer a…
View article: Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R Open
Recommender systems often rely on sub-symbolic machine learning approaches that operate as opaque black boxes. These approaches typically fail to account for the cognitive processes that shape user preferences and decision-making. In this …
View article: The climate gluing protests: analyzing their development and framing in media since 1986 using sentiment analyses and frame detection models
The climate gluing protests: analyzing their development and framing in media since 1986 using sentiment analyses and frame detection models Open
Recent climate-related protests by social movements such as Extinction Rebellion, Just Stop Oil , and others have included actions like defacing artwork and gluing oneself to objects and streets. Using sentiment analysis and frame detectio…
View article: First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024)
First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024) Open
In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change. With a foundation in AI and data analytics, Recommender Systems can be effective…
View article: Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models Open
Users' interaction or preference data used in recommender systems carry the risk of unintentionally revealing users' private attributes (e.g., gender or race). This risk becomes particularly concerning when the training data contains user …
View article: Hiding Your Awful Online Choices Made More Efficient and Secure: A New Privacy-Aware Recommender System
Hiding Your Awful Online Choices Made More Efficient and Secure: A New Privacy-Aware Recommender System Open
Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity data where, if the data is not adequately protected, const…
View article: FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines
FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines Open
Revealing the framing of news articles is an important yet neglected task in\ninformation seeking and retrieval. In the present work, we present FrameFinder,\nan open tool for extracting and analyzing frames in textual data. FrameFinder\nv…
View article: Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media
Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media Open
Understanding how online media frame issues is crucial due to their impact on public opinion. Research on framing using natural language processing techniques mainly focuses on specific content features in messages and neglects their narra…
View article: The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias Open
Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is …
View article: The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias Open
Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often random noise is added to the data to ensure Differential Privacy (DP). However, to date, it is n…
View article: LFM2b Lyrics Descriptor Analyses
LFM2b Lyrics Descriptor Analyses Open
LFM2b Lyrics Descriptor Analyses This dataset provides lyrics descriptors for 580,000 songs, including lexical, structural, diversity-related, readability, rhyme, structural, and emotional descriptors. This dataset was the basis of an anal…
View article: Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks
Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks Open
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated grea…
View article: Differential privacy in collaborative filtering recommender systems: a review
Differential privacy in collaborative filtering recommender systems: a review Open
State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' pri…
View article: Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks Open
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated grea…
View article: Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation Open
Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intri…
View article: Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives
Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives Open
This tutorial provides an interdisciplinary overview about the topics of fairness, non-discrimination, transparency, privacy, and security in the context of recommender systems. These are important dimensions of trustworthy AI systems acco…
View article: Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Computational Versus Perceived Popularity Miscalibration in Recommender Systems Open
Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which comm…
View article: ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations
ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations Open
User-based KNN recommender systems ( UserKNN ) utilize the rating data of a target user’s k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors, since the recommendations could expose…
View article: Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation Open
This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation". listening_events.tsv.bz2 : Dataset excerpt from LFM-2b, before filteri…
View article: Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation Open
This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation". listening_events.tsv.bz2 : Dataset excerpt from LFM-2b, before filteri…