Describe What You See with Multimodal Large Language Models to Enhance
  Video Recommendations
Authors: name: Marco De Nadai, name: Andreas Damianou, name: Mounia Lalmas

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.