A hybrid recommendation method based on sparse edge denoising and automatic coding
A noise-reduction automatic encoding and hybrid recommendation technology, applied in natural language data processing, marketing, instrumentation, etc., can solve the problems of learning ability and classification accuracy not as good as SmDAE, not considering the problem of silent users, and insufficient learning of hidden features, etc., to achieve Improve algorithm efficiency and recommendation accuracy, improve prediction scoring accuracy, and enhance product feature representation
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[0047] Example 1: Such as figure 1 As shown, a hybrid recommendation method based on sparse edge noise reduction automatic coding includes the following steps:
[0048] Step1: Combine each product review into a review document, use TF-IDF to process the review text of each product, and select the word with the highest TF-IDF value to construct the product content vector as the feature representation of the product;
[0049] Step2: Use the product content vector to train the sparse edge noise reduction automatic coding model, use the trained model to further extract the product features from the product content vector, and use the cosine similarity to calculate the similarity of the product feature vectors to obtain the influence of neighboring products;
[0050] Step3: The influence of neighboring commodities is combined with the user-rating matrix decomposition to obtain the predicted score.
[0051] As a preferred solution of the present invention, the specific steps of Step 1 are:...
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