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A survey on large language models for recommendation
L Wu, Z Zheng, Z Qiu, H Wang, H Gu, T Shen, C Qin… - World Wide Web, 2024 - Springer
Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …
Natural Language Processing (NLP) and have recently gained significant attention in the …
How can recommender systems benefit from large language models: A survey
J Lin, X Dai, Y ** review
P Sweetser - Proceedings of the 6th ACM Conference on …, 2024 - dl.acm.org
Large language models (LLMs) hold interesting potential for the design, development, and
research of video games. Building on the decades of prior research on generative AI in …
research of video games. Building on the decades of prior research on generative AI in …
Prompting large language models for recommender systems: A comprehensive framework and empirical analysis
Recently, large language models such as ChatGPT have showcased remarkable abilities in
solving general tasks, demonstrating the potential for applications in recommender systems …
solving general tasks, demonstrating the potential for applications in recommender systems …
Position: Graph foundation models are already here
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph
domain, aiming to develop graph models trained on extensive and diverse data to enhance …
domain, aiming to develop graph models trained on extensive and diverse data to enhance …
A survey of generative search and recommendation in the era of large language models
With the information explosion on the Web, search and recommendation are foundational
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …
infrastructures to satisfying users' information needs. As the two sides of the same coin, both …
Large language models for intent-driven session recommendations
Z Sun, H Liu, X Qu, K Feng, Y Wang… - Proceedings of the 47th …, 2024 - dl.acm.org
The goal of intent-aware session recommendation (ISR) approaches is to capture user
intents within a session for accurate next-item prediction. However, the capability of these …
intents within a session for accurate next-item prediction. However, the capability of these …