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Found 5 papers, 3 papers with code
Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation
The common research goal of self-supervised learning is to extract a general representation which an arbitrary downstream task would benefit from.
Self-Supervised Multimodal Opinion Summarization
To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum.
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation
To eliminate this issue, we propose a framework called CITIES, which aims to enhance the quality of the tail-item embeddings by training an embedding-inference function using multiple contextual head items so that the recommendation performance improves for not only the tail items but also for the head items.
Freudian and Newtonian Recurrent Cell for Sequential Recommendation
The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings.
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items.
