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Found 13 papers, 3 papers with code
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling
The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions.
Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval
To correlate the context of objects with the textual context, we further refine the visual semantic representation via the cross-level object-sentence and word-image based interactive attention.
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection
While the region-level feature learning from local face patches features via graph neural network can encode the correlation across different AUs, the pixel-wise and channel-wise feature learning via graph attention network can enhance the discrimination ability of AU features from global face features.
Automatic Facial Paralysis Estimation with Facial Action Units
In this paper, we propose a novel Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and use it to classify facial paralysis severity.
Supervised Advantage Actor-Critic for Recommender Systems
However, the direct use of RL algorithms in the RS setting is impractical due to challenges like off-policy training, huge action spaces and lack of sufficient reward signals.
Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval
In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e. g., "dog $\to$ play $\to$ ball" in semantic structure for an image, and (ii) seeking explicit inter-modal structural and semantic correspondence between the visual and textual modalities.
Self-Supervised Reinforcement Learning for Recommender Systems
A major component of RL approaches is to train the agent through interactions with the environment.
Graph Highway Networks
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency.
A Simple Convolutional Generative Network for Next Item Recommendation
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
Batch IS NOT Heavy: Learning Word Representations From All Samples
Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations.
Improving Negative Sampling for Word Representation using Self-embedded Features
Although the word-popularity based negative sampler has shown superb performance in the skip-gram model, the theoretical motivation behind oversampling popular (non-observed) words as negative samples is still not well understood.
