System and method for recommending commodities or services based on product dimensions
A product dimension and recommendation algorithm technology, applied in the field of data processing, can solve problems such as cold start of product information, and achieve the effects of improving recommendation accuracy, strong explanation, and accurate description
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Embodiment 1
[0045] This embodiment provides a method for recommending commodities or services based on product dimensions, including the following steps:
[0046] S1 initialization, screening the product data in the database, obtaining the labels of each product and the corresponding label weight information, and generating a list of product labels;
[0047] S2 uses the recommendation algorithm to calculate and filter the tags selected by the user for the first time, sort the results and display them to the user;
[0048] S3 generates an intelligent recommendation list, sets the user's operations in the program through the intelligent recommendation algorithm rules, and continuously revises the recommended products according to the operation data;
[0049] S4 generates a self-selected recommendation list, and when the recommended product does not satisfy the customer, performs secondary screening on the existing recommended product according to the user screening label.
[0050] In this ...
Embodiment 2
[0055] At the specific implementation level, this embodiment provides a specific implementation of a method for recommending commodities or services based on product dimensions. Refer to figure 1 and figure 2 As shown, the details are as follows:
[0056] In the data standardization process of this embodiment, the product label and label weight rules are set by the person in charge of the scheme. Filter the product data in the database, obtain the tags of each product and the corresponding tag weight information, and generate a list of product tags.
[0057] In this embodiment, the initial product recommendation list is generated, and the person in charge of the project sets the recommendation algorithm model and rules, and then calculates according to the tags selected by the user after entering the program to search for products for the first time, and filters the product tags in the database, and the filtered The products are sorted according to the scores from high to l...
Embodiment 3
[0065] On the basis of embodiment 2, with reference to image 3 As shown, this embodiment further provides a data standardization process as follows:
[0066] The product label specifications and rules are formulated by the project leader. Product tags as 2-tuples:
[0067] T=(I,S),
[0068] in:
[0069] I is a collection of product label information, where i k is the kth label of the product. i=("glass", "straight cup", "transparent color", "high temperature resistance"), indicating that the product label is "glass", "straight cup", "patterned", "high temperature resistance".
[0070] S is a set of product tag weights. Where t is the tag attribute, f is the tag weight attribute, and s is the set of tags with weight information. For example, s=("disinfection", 4.5) indicates that the weight of the label "disinfection" is 4.5 points (using a 5-point scale).
[0071] S(p)={("glass", 5), ("straight cup", 5), (patterned, 2), (high temperature resistant, 4.5)}, indicatin...
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