Metarank guides for WordPress & WooCommerce

A proposal to produce a full-lifecycle LTR with Metarank Labs and Vespa.ai for WordPress

Vespa.ai is the perfect solution for personalized search and recommendations with XGBoost or LightGBM models, but it requires external code to create the models. Metarank Labs is the perfect no-code solution for training LTR models, thanks to its predefined feature store recipes, but it lacks the flexibility of pure vector search to provide perfect accurate results. So we propose to combine the two as the first no-code Learning to Rank solution that supports the full cycle: Ingest items in Metarank and Vespa Ingest events in Metarank Transform items and events with feature store recipes as yaml parameters Train models on the feature store Export the model to Vespa.ai as an internal ONNX embedder Rank/rerank the search/recommendations with ranking expressions including the trained model

Woocommerce recommendation engines

How to add a recommendation engine to your Woocommerce ?

One of the best technologies to boost sales for e-commerce today are recommendation systems. But how could you add a recommendation engine to your woocommerce website?  You’re at the right place. First we will define what recommendation engines are, then we can establish strategies and solutions to add recommendation engines to Woocommerce.   What is a recommendation engine?   A recommendation engine, also called a recommender system, will suggest (or recommend) items that it predicts users will be inclined to view or buy. These predictions are based on patterns of users’ past behaviors and preferences.   This technology is based on personalization and is closely related to personalized search.   What are the benefits of a recommendation engine for Woocommerce?   Recommendations greatly boosts sales