Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations

TL;DR

This paper proposes a zero-finetuning framework that uses multimodal large language models to generate high-level semantic descriptions of videos, significantly improving personalized video recommendations by capturing deeper content understanding.

Contribution

Introducing a simple, recommendation system-agnostic method that leverages off-the-shelf MLLMs to inject high-level semantics into video recommendation pipelines without additional fine-tuning.

Findings

Outperforms traditional features on MicroLens-100K dataset

Enhances recommendation accuracy across multiple models

Demonstrates effectiveness of MLLMs as knowledge extractors

Abstract

Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features describe what appears on the screen but miss deeper semantics such as intent, humour, and world knowledge that make clips resonate with viewers. For example, is a 30-second clip simply a singer on a rooftop, or an ironic parody filmed amid the fairy chimneys of Cappadocia, Turkey? Such distinctions are critical to personalised recommendations yet remain invisible to traditional encoding pipelines. In this paper, we introduce a simple, recommendation system-agnostic zero-finetuning framework that injects high-level semantics into the recommendation pipeline by prompting an off-the-shelf Multimodal Large Language Model (MLLM) to summarise each clip into a rich natural-language description (e.g. "a superhero parody with slapstick fights and orchestral stabs"), bridging the gap between raw content and user intent. We use MLLM output with a state-of-the-art text encoder and feed it into standard collaborative, content-based, and generative recommenders. On the MicroLens-100K dataset, which emulates user interactions with TikTok-style videos, our framework consistently surpasses conventional video, audio, and metadata features in five representative models. Our findings highlight the promise of leveraging MLLMs as on-the-fly knowledge extractors to build more intent-aware video recommenders.