Real-time goods recommendation method based on customer shopping intention exploration

Abstract

The invention discloses a real-time goods recommendation method based on customer shopping intention exploration. The method comprises the following steps: 1, constructing a tree-shaped customer shopping intention model; 2, detecting user behaviors in real time and identifying user intention; 3, detecting the change of the user intention in real time; 4, tracking the change track of the user intention and predicting the deep intention of the user: predicting the deep intention of the user based on the discrete intention of the user; and 5, determining a goods recommendation strategy: determining the goods recommendation strategy according to the shopping intention of the customer. The method disclosed by the invention automatically identifies the shopping intention of the customer, performs precise individualized recommendation, and improves the customer satisfaction; and moreover, the method automatically detects the change of the shopping intention of the customer and adjusts the goods recommendation strategy. The method adapts to the changing interest of the customer in real time, deeply explores the shopping intention of the customer based on the user intention model and provides professional recommendation service to the user.

Description

technical field [0001] The present invention relates to the application in the field of e-commerce, in particular to a real-time product recommendation method based on customer shopping intention mining. Background technique [0002] Similar technologies closest to the present invention can be roughly divided into two categories: [0003] (1) Research on shopping intention of e-commerce. [0004] (2) Research on personalized recommendation of e-commerce. [0005] The above two technologies and their shortcomings are introduced respectively as follows: [0006] (1) Research on shopping intention of e-commerce. [0007] From a commercial point of view, study the reasons why customers choose e-commerce. The research method is to conduct statistical analysis on the research data through questionnaires, interviews, etc., and draw conclusions. For example, relevant literature divides e-commerce shopping intentions into 12 categories through research methods such as surveys an...

AI Technical Summary

Problems solved by technology

The recommendation based solely on product information, because there are many product attributes, the system cannot obtain the customer's interest, and there is a large blindness; the recommendation combined with the user's historical behavior and preferences, because the customer's historical behavior cannot accurately reflect the customer's real-time shopping intention, Also not suitable for recommendations in the customer's instant shopping process

[0011] At present, many e-commerce websites use personalized recommendation based on collaborative filtering. Due to problems such as cold start and data sparseness, it is also impossible to provide customers with ideal real-time recommendation results.

[0012] In short, the current research cannot provide customers with accurate real-time recommendations

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