E-commerce multimodal recommendation system implementation scheme
Building an e-commerce recommendation system with Vespa.ai can be broken down into the following key steps:
- Multimodal data modeling: Multi-dimensional features such as text (title/description), vectors (image/video embedding), and structured data (price/category) should be included when defining the commodity data model
- Hybrid Query Design: Combine multiple search criteria using Vespa's proprietary YQL syntax, e.g., match text keywords and visual similarity vectors simultaneously
- Real-time personalized sorting: Integrate recommendation models in TensorFlow or ONNX format to dynamically adjust presentation weights
Specific implementation process:
- Prepare commodity data and convert to multimodal features (text vectors can be modeled with BERT-like models, images with CV models such as ResNet)
- Declare each field type and indexing method when configuring the data schema, as shown in the following example:
{
"fields": [
{ "name": "title", "type": "string", "index": "enable" }, "label". "index": "enable" },
{ "name": "image_embedding", "type": "tensor(x[512])" }
]} - Deploy recommendation model and set ranking configuration to realize the integrated service of "search + recommendation".
Typical application scenario: when users search for "red dress", the system can not only hit the keyword commodity, but also based on the user's historical behavior to recommend the visual style of similar styles, to enhance the conversion rate of 30%+.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
May not be reproduced without permission:AI productivity tools " How to implement a hybrid recommendation system based on multimodal data in an e-commerce platform?
