Building Smarter Visual Recommendations with Gemini Multimodal Embeddings

2026-05-14 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

This article compares the performance of Gemini multimodal embeddings against ResNet50 and SigLIP for building visual recommendation and search systems within Elasticsearch. Previous work showed ResNet50 struggled with semantic relevance, necessitating a Filtered k-NN layer in Elasticsearch. SigLIP improved recommendations without requiring this additional filtering. The current experiment leverages the Gemini Embedding API via Google AI Studio and the GenAI Python SDK to extract embeddings. The goal is to evaluate Gemini's results against the previously used ResNet and SigLIP models, specifically focusing on its ability to capture aesthetic and semantic understanding for smarter visual recommendations.

Key takeaway

For AI Engineers building visual recommendation systems, consider integrating the Gemini Embedding API. Its multimodal capabilities can improve semantic relevance and aesthetic understanding, potentially reducing the need for complex filtering layers like Filtered k-NN in Elasticsearch. This approach streamlines development and enhances recommendation quality, making your systems more effective.

Key insights

Gemini multimodal embeddings offer improved semantic understanding for visual recommendation systems compared to ResNet50 and SigLIP.

Principles

• Multimodal embeddings enhance aesthetic understanding.
• API-based embeddings simplify model integration.

Method

The method involves extracting image embeddings using the Gemini Embedding API via Google AI Studio and GenAI Python SDK, then evaluating these embeddings against ResNet50 and SigLIP within an Elasticsearch recommendation system.

In practice

• Use Gemini API for visual embedding extraction.
• Integrate embeddings into Elasticsearch for k-NN search.

Topics

• Gemini Multimodal Embeddings
• Visual Recommendation Systems
• Elasticsearch
• SigLIP
• ResNet50

Best for: Machine Learning Engineer, AI Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.