CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation

Understand

Traditional recommender systems such as matrix factorization methods have primarily focused on learning a shared dense embedding space to represent both items and user preferences.

• Subsequently, sequence models such as RNN, GRUs, and, recently, Transformers have emerged and 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.