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October 03, 2023
As practitioners of Recommender Systems (RecSys) in different industries and fields, we often find situations where explicit feedback is a rarity. Imagine a scenario in which the only information available is product transaction history data: products a customer purchased and when they purchased them. What is lacking is information on products the customer perused, but rejected. In this implicit feedback dataset, we have positive feedback only (purchases), and all non-purchases are a mix of unknown (products customers haven’t seen) and true negative feedback (products customers have seen but didn’t like). This poses a challenge when building a recommender model that predicts a customer’s propensity to purchase other products.
