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Found 7 papers, 4 papers with code
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs).
Aligning Language Models for Versatile Text-based Item Retrieval
Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks.
MISS: A Generative Pretraining and Finetuning Approach for Med-VQA
However, most methods in the medical field treat VQA as an answer classification task which is difficult to transfer to practical application scenarios.
RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability
Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training.
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
In this paper, we bridge the gap between recommender models and LLMs, combining their respective strengths to create a versatile and interactive recommender system.
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence.
Ered: Enhanced Text Representations with Entities and Descriptions
On the one hand, it is implicit and only model weights are paid attention to, the pre-trained entity embeddings are ignored.
