Recommendations rely on supervised machine learning models that are trained on your product data and user interactions.
Recommend uses two different algorithm types: collaborative filtering and content-based filtering.
Collaborative filtering analyzes user events from the last 30-90 days. Recommend creates a table of userToken and objectID which show how many times each user interacted with each record (object). Recommend then uses a collaborative filtering algorithm to find other records that are similar or frequently bought together:
Similar if the same set of users interacts with them.
Frequently bought together if the same set of users bought them.
Content-based filtering analyzes key attributes of items, such as their titles or descriptions, to find similarities.
original poster: abababa
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posted 2025-3-29 01:26
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