Provided is a marketing product recommendation method, comprising: after receiving a user's request for marketing product data, acquiring the user features of the user and the product features of each marketing product; generating overlapping features based on the user features and the product features; inputting the user features, the product features and the overlapping features into a click-rate evaluation model to obtain a click-rate evaluation value of the user for each marketing product, wherein the click-rate evaluation model is a machine learning model, and is trained by using user features and marketing product feature samples with a known click rate; and determining, according to the click rate evaluation value, the M marketing products, and returning data of the M marketing products to the user, wherein M is a natural number.
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