Recommending method and recommending system

Abstract

The invention discloses a recommending method and a recommending system. The recommending method includes the following steps of firstly, computing likeness of target users for various attributions of a commodity to be recommended; secondly, integrating likeness of the target users for various attributions of the commodity to obtain the integral likeness of the target users to the commodity; and thirdly, recommending the corresponding commodity to the target users according to the likeness of the target users for the commodity to be recommended. In order to provide more accurate recommendations for new users and users having low purchase quantity, the integral tendency of all users and personal tendency of a single user are comprehensively analyzed by the Bayes method. For optimization of the new users and the users having low purchase quantity, service quality is improved favorably, and searching time of the users is saved.

Description

technical field [0001] The invention relates to the technical field of network information retrieval, in particular to a recommendation method and system. Background technique [0002] With the popularization of the Internet and the development of e-commerce, recommendation systems are widely used and become an important content of network information retrieval technology. The application of a good recommendation system saves a lot of time for users, because they can quickly find what they need based on the content recommended by the recommendation system, without wasting time by doing a lot of searching in massive commodities or data. [0003] The personalized recommendation system is an advanced business intelligence platform based on massive data mining to help e-commerce sites provide fully personalized decision support and information services for their customers. The recommendation system of the shopping website recommends products for customers, automatically complet...

AI Technical Summary

Problems solved by technology

[0009] 1) The user's evaluation of the product is very sparse, so the similarity between users based on the user's evaluation may not be accurate (that is, the sparsity problem);

[0010] 2) With the increase of users and commodities, the performance of the system will become lower and lower (that is, scalability issues);

[0011] 3) If no user has ever commented on a product, the product cannot be recommended (that is, the initial evaluation problem)

[0012] It can be seen that the defects in the above-mentioned existing recommendation systems and methods will eventually affect the recommendation results, resulting in incomplete or inaccurate recommendations.

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