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Found 7 papers, 0 papers with code
A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems
Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload.
Selecting the suitable resampling strategy for imbalanced data classification regarding dataset properties
These models allow us to check several factors simultaneously considering a wide range of values since they are induced from very varied datasets that cover a broad spectrum of conditions.
Dynamic inference of user context through social tag embedding for music recommendation
This work involves the proposal of a method for the detection of the user contextual state when listening to music based on the social tags of music items.
Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach
In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations.
Sentiment Analysis Based on Deep Learning: A Comparative Study
In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP.
Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach
We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods.
A session-based song recommendation approach involving user characterization along the play power-law distribution
In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users' streaming sessions.
