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Found 9 papers, 8 papers with code
RecLM: Recommendation Instruction Tuning
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions.
DiffMM: Multi-Modal Diffusion Model for Recommendation
To address this limitation, recent research has introduced self-supervised learning techniques to enhance recommender systems.
Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction
Specifically, our framework involves a symmetrical contrastive objective that encompasses both sentence-anchored and label-anchored contrastive losses.
GraphPro: Graph Pre-training and Prompt Learning for Recommendation
The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training.
Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect.
Debiased Contrastive Learning for Sequential Recommendation
This solution is designed to tackle the popularity bias issue in recommendation systems.
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer
Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.
Fast and Effective Global Covariance Pooling Network for Image Steganalysis
To make a good tradeoff between training time and performance, we carefully design the architecture of the proposed network according to our extensive experiments.
Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning
Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace.
