Search Results for author:
Found 37 papers, 23 papers with code
Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences.
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization
Language models frequently inherit societal biases from their training data.
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
We show that SiBraR's recommendations are accurate in missing modality scenarios, and that the model is able to map different modalities to the same region of the shared embedding space, hence reducing the modality gap.
The Importance of Cognitive Biases in the Recommendation Ecosystem
Cognitive biases have been studied in psychology, sociology, and behavioral economics for decades.
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems
In addition, users from less represented countries (e. g., Finland) are, in the long term, most affected by the under-representation of their local music in recommendations.
Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts.
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation
We introduce a new model - Segment any Text (SaT) - to solve this problem.
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
Previous efforts to address this issue have added or removed parts of users' preferences prior to or during model training to improve privacy, which often leads to decreases in recommendation accuracy.
Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan
The Face-voice Association in Multilingual Environments (FAME) Challenge 2024 focuses on exploring face-voice association under a unique condition of multilingual scenario.
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
Bias mitigation of Language Models has been the topic of many studies with a recent focus on learning separate modules like adapters for on-demand debiasing.
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
In this work, we study how DP impacts recommendation accuracy and popularity bias, when applied to the training data of state-of-the-art recommendation models.
Domain Information Control at Inference Time for Acoustic Scene Classification
In the Acoustic Scene Classification task (ASC), domain shift is mainly caused by different recording devices.
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing.
ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process.
Unlearning Protected User Attributes in Recommendations with Adversarial Training
We conduct experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the effectiveness of the bias mitigation method based on the inability of external attackers in revealing the users' gender information from the model.
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks.
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?
This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators.
Explainability in Music Recommender Systems
In this article, we discuss how explainability can be addressed in the context of MRSs.
Collaborative Filtering
Explainable artificial intelligence
+3
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries.
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?
In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's tau rank-order correlation).
Predicting Music Relistening Behavior Using the ACT-R Framework
In this paper, we describe a psychology-informed approach to model and predict music relistening behavior that is inspired by studies in music psychology, which relate music preferences to human memory.
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges
Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content.
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty.
Current Challenges and Future Directions in Podcast Information Access
Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators.
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers
In this work, we first provide a novel framework to measure the fairness in the retrieved text contents of ranking models.
TripClick: The Log Files of a Large Health Web Search Engine
As such, the collection is one of the few datasets offering the necessary data richness and scale to train neural IR models with a large amount of parameters, and notably the first in the health domain.
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners
In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided.
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences.
Do Neural Ranking Models Intensify Gender Bias?
Concerns regarding the footprint of societal biases in information retrieval (IR) systems have been raised in several previous studies.
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
In this paper, we introduce a psychology-inspired approach to model and predict the music genre preferences of different groups of users by utilizing human memory processes.
Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young
In this paper, we analyze a large dataset of user-generated music listening events from Last. fm, focusing on users aged 6 to 18 years.
Collaborative Filtering
Cultural Vocal Bursts Intensity Prediction
+1
Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems
We conduct rating prediction experiments in which we tailor recommendations to a user's level of preference for the music mainstream using the proposed 6 mainstreaminess measures.
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items.
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features
To raise awareness of this fact, we investigate differences between explicit user preferences and implicit user profiles.
Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations
Music recommender systems have become central parts of popular streaming platforms such as Last. fm, Pandora, or Spotify to help users find music that fits their preferences.
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation
We further report and analyze the results obtained by the top performing teams in each track and explore the approaches taken by the winners.
