  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, researchers have
recently explored using LLMs that are pretrained on vast corpora of text for
sequential recommendation. To use LLMs in sequential recommendations, both the
history of user interactions and the model's prediction of the next item are
expressed in text form. We propose CALRec, a two-stage LLM finetuning framework
that finetunes a pretrained LLM in a two-tower fashion using a mixture of two
contrastive losses and a language modeling loss: the LLM is first finetuned on
a data mixture from multiple domains followed by another round of target domain
finetuning. Our model significantly outperforms many state-of-the-art baselines
(+37% in Recall@1 and +24% in NDCG@10) and systematic ablation studies reveal
that (i) both stages of finetuning are crucial, and, when combined, we achieve
improved performance, and (ii) contrastive alignment is effective among the
target domains explored in our experiments.
