New cigarette product recommendation method and recommendation system

Abstract

The invention discloses a new cigarette product recommendation method and system, and the method comprises the steps: obtaining a user list which at least comprises the ID of all users in a target region and the information of cigarettes sold by each user; calculating the similarity between the cigarette information of the cigarettes of each specification and the new product information of the new product to be cultivated, and obtaining a preset number of cigarette specifications according to the sequence of the similarity from high to low; and searching a target user in all the user IDs, and recommending the new product to be cultivated to the target user, wherein the target user is a user who orders any cigarette product specification. According to the technical scheme provided by the invention, more accurate digital recommendation of new cigarette products is realized based on big data.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a method and system for recommending new cigarette products. Background technique [0002] The cultivation of new cigarette products is an important link in the cultivation of product regulations, and it is also a marketing pain point faced by various units in the industry. The traditional new product cultivation model is mainly through the four-step method of publishing plans, launching at fixed points, collecting information, and spreading out points. Relevant market data monitoring and repeated trial sales experiments not only waste a lot of manpower, material resources, and financial resources, but also rely on human experience to judge the location of new products. The users exposed to relevant promotional information are not the target users. The target user positioning is inaccurate and increases the excess expenditure of the enterprise, so that new products can...

AI Technical Summary

Problems solved by technology

The traditional new product cultivation model is mainly through the four-step method of publishing plans, launching at fixed points, collecting information, and spreading out points. Relevant market data monitoring and repeated trial sales experiments not only waste a lot of manpower, material resources, and financial resources, but also rely on human experience to judge the location of new products. The users exposed to relevant promotional information are not the target users. The target user positioning is inaccurate and increases the excess expenses of the enterprise, so that new products cannot be pushed accurately, and it is often difficult to achieve the expected results.

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