Search Results for author:
Found 11 papers, 8 papers with code
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w. r. t augmentations through within-behavior contrastive learning.
Scalable and Effective Generative Information Retrieval
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
Adaptive Test-Time Personalization for Federated Learning
To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains.
Language Models As Semantic Indexers
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications.
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).
Comprehensive Fair Meta-learned Recommender System
The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively.
Neural Collaborative Filtering Bandits via Meta Learning
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.
Causal Intervention for Leveraging Popularity Bias in Recommendation
This work studies an unexplored problem in recommendation -- how to leverage popularity bias to improve the recommendation accuracy.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.
