An association rule recommendation method based on adaptive multi-minimum support

Abstract

The invention discloses an association-rule recommending method based on self-adaptive multiple minimum supports. The method comprises the following steps of firstly, establishing a commodity-classifying hierarchical tree according to commodity classification, and classifying concrete commodities according to the classifying hierarchical tree; next respectively carrying out minimum-support threshold-value setting on each concrete commodity and the upper-layer class of a concrete-commodity layer, and then mining frequent item sets and generating rules by utilizing a multiple-minimum-support association-rule expanding algorithm on the basis of the support threshold-value setting, wherein the threshold-value setting relates to the influences of time factors, concrete-commodity price factors and concrete-commodity brand factors; finally generating recommendation for each user by adopting a TOP-N recommending method. When personalized recommendation is made for the user by the association-rule recommending method, the characteristics of different objects can be better embodied by considering the influences of many factors on the multiple-minimum-support threshold-value setting for the concrete commodities and the classes; meanwhile, a data-sparsity problem and a cold-starting problem in a recommending system are relieved, so that the personalized recommendation can be more accurately made for the user.

Description

technical field [0001] The invention discloses an association rule recommendation method based on self-adaptive multi-minimum support, in particular relates to a method for recommending personalized commodities to a specific user, and belongs to the technical field of recommendation systems. Background technique [0002] Personalized recommendation is to recommend information and products that the user is interested in based on the user's interest characteristics and purchase behavior. With the continuous expansion of the scale of e-commerce and the rapid growth of the number and types of commodities, customers need to spend a lot of time to find the commodities they want to buy. This process of browsing a large amount of irrelevant information and products will undoubtedly cause consumers who are submerged in the problem of information overload to continue to lose. In order to solve these problems, personalized recommendation system came into being. The personalized recom...

AI Technical Summary

Problems solved by technology

[0014] The technical problems to be solved by the present invention are: for traditional recommendation algorithms relying on user ratings, recommendation results are sensitive to data sparsity and cold start problems, and traditional association rule algorithms set a single unified support for all commodities that only depends on the frequency of occurrence of commodities degree problem, an association rule recommendation method based on adaptive multi-minimum support degree is proposed. In the process, an adaptive support degree threshold is generated for each product and category, and more meaningful association rules are mined out to make user recommendations. more accurate recommendation

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