Domain shift is considered a challenge in machine learning as it causes
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Recent research has suggested different metrics to measure the inconsist...
Large pre-trained language models contain societal biases and carry alon...
User-based KNN recommender systems (UserKNN) utilize the rating data of ...
Collaborative filtering algorithms capture underlying consumption patter...
This work investigates the effect of gender-stereotypical biases in the
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The most common way to listen to recorded music nowadays is via streamin...
The provided contents by information retrieval (IR) systems can reflect ...
Several studies have identified discrepancies between the popularity of ...
Providing suitable recommendations is of vital importance to improve the...
The music domain is among the most important ones for adopting recommend...
Existing neural ranking models follow the text matching paradigm, where
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Podcasts are spoken documents across a wide-range of genres and styles, ...
Societal biases resonate in the retrieved contents of information retrie...
Click logs are valuable resources for a variety of information retrieval...
Music recommender systems have become an integral part of music streamin...
Music preferences are strongly shaped by the cultural and socio-economic...
Concerns regarding the footprint of societal biases in information retri...
In this paper, we introduce a psychology-inspired approach to model and
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In this paper, we analyze a large dataset of user-generated music listen...
Popularity-based approaches are widely adopted in music recommendation
s...
In order to improve the accuracy of recommendations, many recommender sy...
Music recommender systems have become central parts of popular streaming...
The ACM Recommender Systems Challenge 2018 focused on the task of automa...
The automated generation of music playlists can be naturally regarded as...
Automated music playlist continuation is a common task of music recommen...
Music recommender systems (MRS) have experienced a boom in recent years,...