CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation

arXiv:2405.02429v1 Announce Type: cross
Abstract: Traditional recommender systems such as matrix factorization methods rely on learning a shared dense embedding space to represent both items and user preferences. Sequence models such as RNN, GRUs, and, recently, Transformers have also excelled in the task of sequential recommendation. This task requires understanding the sequential structure present in users' historical interactions to predict the next item they may like. Building upon the success of Large Language Models (LLMs) in a variety of tasks, …

abstract alignment arxiv cs.ai cs.cl cs.ir cs.lg embedding factorization generative llms matrix recommendation recommender systems rnn space systems transformers type understanding