Integrating Large Language Models with Graphical Session-Based Recommendation

arXiv:2402.16539v1 Announce Type: cross
Abstract: With the rapid development of Large Language Models (LLMs), various explorations have arisen to utilize LLMs capability of context understanding on recommender systems. While pioneering strategies have primarily transformed traditional recommendation tasks into challenges of natural language generation, there has been a relative scarcity of exploration in the domain of session-based recommendation (SBR) due to its specificity. SBR has been primarily dominated by Graph Neural Networks, which have achieved many successful outcomes due to their …

abstract arxiv capability challenges context cs.cl cs.ir cs.lg development exploration language language generation language models large language large language models llms natural natural language natural language generation recommendation recommender systems session strategies systems tasks type understanding