A Content-Based Matching and Recommendation Method for User Personalized Products

Abstract

The invention discloses a content-based matching recommendation method for user personalized products, which overcomes the problem that the recommendation algorithm in the prior art still needs to be improved. The invention includes step 1, user-based random batch sampling method; step 2, content-based user-product matching method: establish a network; according to batch input based on user-based random batch sampling method, orderly user ids are obtained, and a list of product ids is recorded in history , a set of target product id and label, train, adjust, and evaluate the network model on the training set, verification set, and test set respectively; input a specific user and its history, and use the content-based user-product matching network to predict its response to all unwatched Score and sort the movies, and finally output top-N recommendation results. The invention adopts a lightweight neural network, which greatly reduces the training time and training equipment requirements, the sampling process is easy, the model input is more random, and the generalization ability of the prediction result is stronger.

Description

technical field [0001] The invention relates to the field of recommendation algorithms, in particular to a method for matching and recommending content-based user personalized products. Background technique [0002] With the development of the Internet age, the phenomenon of information overload is becoming more and more serious, users are faced with more and more product choices, and the competition between products is getting bigger and bigger. A recommendation algorithm is an algorithm that matches users and products. A good recommendation algorithm can not only save user time and increase user satisfaction, but also increase product acceptance rate and drive transaction growth. [0003] Existing recommendation algorithms generally use collaborative filtering algorithms to pre-train the user-product interaction information matrix to obtain product latent factors and user latent factors, and then use the pre-trained latent factors to train the recommendation model to obta...

AI Technical Summary

Problems solved by technology

[0005] The present invention overcomes the problem that the recommendation algorithm still needs to be improved in the prior art, and provides a content-based user personalized product matching recommendation method with short training time

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