Big data-based page customization method

Abstract

The invention provides a big data-based page customization method. The method comprises the following steps of: 1, user information acquisition; 2, user data analysis; 3, information push: pushing information to users according to an information matching model when the users browse webpages; and 4, information check. The method provided by the invention has the following beneficial effects of: obtaining the requirements of the users through analyzing the user data, so as to improve the efficiency of obtaining the required information and pushing the information by the users, avoid plenty of irrelevant information, in browsing, caused by searching required commodities by the users in e-commerce, greatly improve the using experience of the users, effectively guide the shopping of the users and satisfy the shopping experience of the users; pushing interested news to the users according to the information matching model and threshold values when the users browse news webpages; and pushing keywords which are possibly searched to the users according to the information matching model and threshold values when the users search webpages, so as to increase the user experience.

Description

Technical field [0001] The invention is a page customization method based on big data, and belongs to the field of big data information processing. Background technique [0002] The Internet is like a double-edged sword, although to a large extent it has promoted the rapid development of e-commerce, enabling merchants to display their products to consumers through e-commerce platforms, and consumers can have complete product information without leaving home. To master and conclude transactions with merchants, both parties take what they need, but with the continuous expansion of e-commerce scale, the number and types of products are growing rapidly, and customers need to spend a lot of time to find the products they want to buy. [0003] This process of browsing a large amount of irrelevant information and products will undoubtedly cause the loss of consumers who are submerged in the problem of information overload. At the same time, as the e-commerce system is constantly expandin...

AI Technical Summary

Problems solved by technology

[0002] The Internet is like a double-edged sword, although to a large extent it promotes the rapid development of e-commerce, enabling merchants to display their products to consumers through e-commerce platforms, and consumers can fully understand the product information without leaving home. Master and conclude a transaction with the merchant, both parties get what they need, but with the continuous expansion of e-commerce scale, the number and types of goods increase rapidly, customers need to spend a lot of time to find the goods they want to buy

[0003] This process of browsing a large amount of irrelevant information and products will undoubtedly lead to the loss of consumers who are submerged in the problem of information overload.

At the same time, as the e-commerce system is constantly expanding and the system structure is becoming more and more complex, the number of users and products is almost on a straight line. However, many recommendation algorithms at this stage have two problems due to their own constraints: Sparsity problem and scalability problem, which seriously affect the quality of recommendation

However, the current technology ignores the impact of the specific environment on user data

On the other hand, in the face of a large number of resources, the existing scheme generates pushes based on users' evaluation information on resources. This kind of push based on page scores can only reflect the user's overall interest in the page.

However, in fact, the user's evaluation of the page resource is often generated according to its attributes, so the push results based only on the user's overall score on the resource are often one-sided.