User modeling and personalized product recommendation system and method

A recommendation system (100) for use in the machinery and power systems industry to provide spare part recommendations to users, characterized by comprising

• at least one database (10) containing spare part and machine model information belonging to the user,
• at least one preprocessing layer (20) that filters out non-qualitative data such as parts for machines not present in the customer's fleet, consumables (oil, wiper fluid), etc., from the database (10), thereby separating qualitative data like spare part information for machines in the customer's fleet,
• at least one algorithm layer (30) that selects the appropriate machine learning algorithm for the qualitative data obtained from the preprocessing layer (20) and performs the algorithm calculations,
• at least one storage layer (40) that filters the results produced by the algorithm layer (30) according to general recommendation, material suggestion, and basket suggestion rules, updates the calculation results based on the customer's real-time actions on the application, and consequently recommends the necessary spare part to the users.