Vector Search

Search that finds

Vector search, also known as vector similarity search or vector-based retrieval, is an advanced search technology used in various applications, including ecommerce websites. It leverages vector embeddings to find and rank relevant items based on their similarity in a high-dimensional space.

"Our database uses advanced models trained on millions of texts to create precise vector representations of your products. This ensures highly relevant search results, improving user satisfaction." Head of ML, Nibelung

How vector search works

1. Vector Embeddings:
   • Products, text descriptions, images, and other data are converted into vectors using machine learning models, often based on deep learning techniques. These models can capture semantic meaning, context, and various features of the items.
2. High-Dimensional Space:
   • Each item is represented as a point in a high-dimensional vector space. The dimensions of this space correspond to the features captured by the embedding model.
3. Similarity Measurement:
   • When a search query is entered, it is also converted into a vector. The search system then measures the similarity between this query vector and the vectors of the items in the database. Common similarity measures include cosine similarity, Euclidean distance, and dot product.
4. Ranking and Retrieval:
   • Items are ranked based on their similarity scores to the query vector. The most similar items (i.e., those with the highest similarity scores or lowest distances) are returned as the search results.

Benefits for Ecommerce

1. Improved Search Relevance:
   • Vector search can better understand the intent behind a user's query, leading to more relevant and accurate search results compared to traditional keyword-based search methods.
2. Personalization:
   • By using vector embeddings that capture user preferences and behaviors, ecommerce websites can provide personalized search results and recommendations, enhancing the user experience.
3. Handling Synonyms and Variations:
   • Vector search can understand and match synonyms or variations of words, allowing users to find products even if they use different terminology.
4. Image and Multi-Modal Search:
   • Vector search supports image search and multi-modal search (combining text and images). For example, users can upload a picture of a product they like, and the system can find visually similar items in the catalog.
5. Scalability:
   • With efficient indexing and search algorithms, vector search can handle large catalogs with millions of products, making it suitable for big ecommerce platforms.
6. Enhanced User Experience:
   • By delivering more accurate and personalized search results, vector search can increase user satisfaction and engagement, potentially leading to higher conversion rates and sales.

Implementation considerations

• Data Preparation:
  • Converting product data into vector embeddings requires a robust preprocessing pipeline and suitable machine learning models.
• Infrastructure:
  • Vector search can be computationally intensive, requiring scalable infrastructure and efficient indexing techniques to ensure fast query response times.
• Continuous Learning:
  • The embedding models and search algorithms may need continuous tuning and updating to adapt to changing user behavior and new product additions.

Get started with Nibilung

Ready to elevate your on-site search and navigation? Contact us today to learn more about our browse and search re-ranking module and how it can benefit your business.

‍
hello@nibelung.ai