  Sequential recommendation aims to predict users' next interaction with items
based on their past engagement sequence. Recently, the advent of Large Language
Models (LLMs) has sparked interest in leveraging them for sequential
recommendation, viewing it as language modeling. Previous studies represent
items within LLMs' input prompts as either ID indices or textual metadata.
However, these approaches often fail to either encapsulate comprehensive world
knowledge or exhibit sufficient behavioral understanding. To combine the
complementary strengths of conventional recommenders in capturing behavioral
patterns of users and LLMs in encoding world knowledge about items, we
introduce Large Language-Recommendation Assistant (LLaRA). Specifically, it
uses a novel hybrid prompting method that integrates ID-based item embeddings
learned by traditional recommendation models with textual item features.
Treating the "sequential behaviors of users" as a distinct modality beyond
texts, we employ a projector to align the traditional recommender's ID
embeddings with the LLM's input space. Moreover, rather than directly exposing
the hybrid prompt to LLMs, a curriculum learning strategy is adopted to
gradually ramp up training complexity. Initially, we warm up the LLM using
text-only prompts, which better suit its inherent language modeling ability.
Subsequently, we progressively transition to the hybrid prompts, training the
model to seamlessly incorporate the behavioral knowledge from the traditional
sequential recommender into the LLM. Empirical results validate the
effectiveness of our proposed framework. Codes are available at
https://github.com/ljy0ustc/LLaRA.
