E-commerce Search Pain Points
E-commerce product search requires balancing text matching (e.g., product names) with semantic similarity (e.g., product attributes). Orama's hybrid search perfectly addresses this challenge.
program of implementation
- Data Model DesignRecommendations: Include text fields (title, description) and vector fields (product feature embeddings) in the schema. Price, category, etc., can be used for pre-filtering.
- Vector GenerationGenerate multimodal embedding vectors from product images and descriptions, such as the 512-dimensional vectors produced by the CLIP model.
- Weight TuningDetermine the optimal weight ratio between text search and vector search through A/B testing, typically starting with a 1:1 ratio for experimentation.
- Results Sorting: Combine Orama's relevance scores with business logic (such as sales volume and ratings) to determine the final ranking.
enhancement
- realizationSearch RecommendationsBased on user query logs, search suggestions are provided using Orama's term frequency statistics.
- increasespelling toleranceSet an appropriate Levenshtein distance threshold to enhance error tolerance.
- integrated (as in integrated circuit)Geographic searchFilter products based on user location to prioritize local items.
Business Effect
By combining hybrid search, both search accuracy and recall can be improved simultaneously: text matching ensures precision, while vector search enhances coverage for long-tail queries. Practical cases demonstrate that this approach can increase search conversion rates by 15-30%.
This answer comes from the articleOrama: a high-performance full-text book and vector search engineThe
May not be reproduced without permission:AI productivity tools " How to Improve Product Retrieval with Orama's Hybrid Search in Ecommerce Apps?
