Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary
  Case Study
Peilin Zhou Meng Cao You-Liang Huang Qichen Ye Peiyan Zhang Junling Liu Yueqi Xie Yining Hua Jaeboum Kim
Large Multimodal Models (LMMs) have demonstrated impressive performance
across various vision and language tasks, yet their potential applications in
recommendation tasks with visual assistance remain unexplored. To bridge this
gap, we present a preliminary case study investigating the recommendation
capabilities of GPT-4V(ison), a recently released LMM by OpenAI. We construct a
series of qualitative test samples spanning multiple domains and employ these
samples to assess the quality of GPT-4V's responses within recommendation
scenarios. Evaluation results on these test samples prove that GPT-4V has
remarkable zero-shot recommendation abilities across diverse domains, thanks to
its robust visual-text comprehension capabilities and extensive general
knowledge. However, we have also identified some limitations in using GPT-4V
for recommendations, including a tendency to provide similar responses when
given similar inputs. This report concludes with an in-depth discussion of the
challenges and research opportunities associated with utilizing GPT-4V in
recommendation scenarios. Our objective is to explore the potential of
extending LMMs from vision and language tasks to recommendation tasks. We hope
to inspire further research into next-generation multimodal generative
recommendation models, which can enhance user experiences by offering greater
diversity and interactivity. All images and prompts used in this report will be
accessible at https://github.com/PALIN2018/Evaluate_GPT-4V_Rec.