Handling common recommender system data sources | Algolia

This article delves into the common data sources required for collaborative filtering type of recommender systems, which are a crucial component of high-performance recommender systems. The inputs for such a system include users, items, and ratings. Three types of recommender system datasets that need to be prepared for a collaborative filtering system are: 1) Items Dataset, 2) Users Dataset, and 3) Interactions Dataset. The article provides detailed steps on how to extract data from various sources like Shopify, Magento, WooCommerce, Google Analytics, and Google Merchant Center for each of these datasets. Privacy concerns are also discussed while handling user data. The final piece of the dataset puzzle is generating the interactions dataset using the Google Analytics Reporting API v4.