Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation

TL;DR

Molar introduces a multimodal LLM framework for sequential recommendation that effectively combines textual, non-textual, and collaborative filtering signals to improve personalization and accuracy.

Contribution

It presents a novel multimodal LLM-based recommendation framework that integrates content modalities with collaborative filtering through a post-alignment mechanism.

Findings

Molar outperforms traditional and LLM-based baselines in recommendation accuracy.

The framework effectively captures user interests and contextual semantics.

Multimodal content integration enhances recommendation robustness.

Abstract

Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more recently, to large language models (LLMs). While the adoption of LLMs has driven substantial advancements, these models inherently lack collaborative filtering information, relying primarily on textual content data neglecting other modalities and thus failing to achieve optimal recommendation performance. To address this limitation, we propose Molar, a Multimodal large language sequential recommendation framework that integrates multiple content modalities with ID information to capture collaborative signals effectively. Molar employs an MLLM to generate unified item representations from both textual and non-textual data, facilitating comprehensive multimodal modeling and enriching item embeddings. Additionally, it incorporates collaborative filtering signals through a post-alignment mechanism, which aligns user representations from content-based and ID-based models, ensuring precise personalization and robust performance. By seamlessly combining multimodal content with collaborative filtering insights, Molar captures both user interests and contextual semantics, leading to superior recommendation accuracy. Extensive experiments validate that Molar significantly outperforms traditional and LLM-based baselines, highlighting its strength in utilizing multimodal data and collaborative signals for sequential recommendation tasks. The source code is available at https://anonymous.4open.science/r/Molar-8B06/.