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Found 11 papers, 10 papers with code
Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
Since previous research has shown that the magnitude of the effect depends on a number of interesting factors such as stimulus complexity and familiarity, leveraging this effect is a way to not only improve repeated recommendation but to gain a more in-depth understanding of both users and stimuli.
Automatic Annotation of Direct Speech in Written French Narratives
The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding.
Attention Mixtures for Time-Aware Sequential Recommendation
Transformers emerged as powerful methods for sequential recommendation.
Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization
Repetition in music consumption is a common phenomenon.
Personalized breath based biometric authentication with wearable multimodality
To be more specific, we design new hardware which consists of an acoustic sensor to collect audio features from the nose, as well as an accelerometer and gyroscope to collect movement on the chest as a result of an individual's breathing.
Improving Transformers with Probabilistic Attention Keys
Inspired by this observation, we propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a mixture of keys at each head.
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
In this paper, we model this cold start similar artists ranking problem as a link prediction task in a directed and attributed graph, connecting artists to their top-k most similar neighbors and incorporating side musical information.
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
Existing extensions of CML also either ignore the heterogeneity of user-item relations, i. e. that a user can simultaneously like very different items, or the latent item-item relations, i. e. that a user's preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with.
A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
This is commonly referred to as the user cold start problem.
Improving Collaborative Metric Learning with Efficient Negative Sampling
Distance metric learning based on triplet loss has been applied with success in a wide range of applications such as face recognition, image retrieval, speaker change detection and recently recommendation with the CML model.
Disambiguating Music Artists at Scale with Audio Metric Learning
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task.
