Shop information recommendation method, device and client

Abstract

The embodiment of the specification provides a shop information recommendation method, device and client. The method includes: determining a corresponding consumption group according with consumptionpreference of a target user; obtaining shop information of consumption already carried out by users in the consumption group, and identifying shop information, of which shop information occurrence frequency is greater than or equal to a preset threshold value, in the shop information; and recommending the shop information of which the occurrence frequency is greater than or equal to the preset threshold value, to the target user.

Description

technical field [0001] The embodiments of this specification relate to the field of Internet communication technology, and in particular to a store information recommendation method, device and client. Background technique [0002] In recent years, with the rapid development of the Internet and the gradual rise of new retail, the Internet has accumulated a large amount of user and store data. Analyzing and mining these data can increase the consumption rate of users in stores. Therefore, using big data, artificial intelligence and other means to carry out online marketing recommendation activities has been valued by more and more businesses. [0003] At present, when performing marketing recommendation processing, the recommendation is often made according to the favorable rate of the store or the transaction volume. For example, if a store has a high praise rate, it will recommend the store with a high praise rate to users. Or, if a store has a high transaction volume, th...

AI Technical Summary

Problems solved by technology

However, in the above-mentioned existing recommendation methods, there is often the case of swiping orders in order to increase the favorable rate or transaction volume, and there are problems of different consumption preferences of different users, resulting in the store recommended to the user is not the store that the user likes, and the recommendation effect is poor.

Images