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Found 20 papers, 9 papers with code
Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization
Large language models (LLMs) have revolutionized the role of AI, yet also pose potential risks of propagating unethical content.
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training
In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels.
Frequency-aware Graph Signal Processing for Collaborative Filtering
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency.
A Comprehensive Summarization and Evaluation of Feature Refinement Modules for CTR Prediction
In addition, we present a new architecture of assigning independent FR modules to separate sub-networks for parallel CTR models, as opposed to the conventional method of inserting a shared FR module on top of the embedding layer.
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
It is crucial to effectively model feature interactions to improve the prediction performance of CTR models.
Ranked #1 on
Click-Through Rate Prediction
on Criteo
Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning
We discovered that most models are essentially misaligned, necessitating further ethical value alignment.
RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents
The rapid evolution of the web has led to an exponential growth in content.
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users.
Recommendation Unlearning via Matrix Correction
We conducted comprehensive experiments to validate the effectiveness of IMCorrect and the results demonstrate that IMCorrect is superior in completeness, utility, and efficiency, and is applicable in many recommendation unlearning scenarios.
Simulating News Recommendation Ecosystem for Fun and Profit
In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms.
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Specifically, TriSIM4Rec consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value decomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs.
Personalized Graph Signal Processing for Collaborative Filtering
However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy.
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e. g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance.
Parameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time.
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance.
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices
Experimental studies with 20 participants demonstrate that, thanks to the emotionship awareness, EMOShip not only achieves superior emotion recognition accuracy over existing methods (80. 2% vs. 69. 4%), but also provides a valuable understanding of the cause of emotions.
Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip
In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH.
MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture
MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets.
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline
By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency.
Mixture-Rank Matrix Approximation for Collaborative Filtering
However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy.
Ranked #4 on
Recommendation Systems
on MovieLens 10M
