Collaborative filtering recommendation method based on user clustering and project association analysis

Abstract

The invention discloses a collaborative filtering recommendation method based on user clustering and project association analysis in order to solve the problems of cold start, data sparseness, low recommendation accuracy and the like of a traditional collaborative filtering recommendation algorithm. According to the method, an improved fuzzy C-means clustering algorithm is adopted to mine the preference degree of hidden features of a user, and an association analysis strategy based on pre-judgment screening is adopted to screen a frequent item set. On the basis, the algorithm uses a user characteristic preference matrix and a user scoring matrix to calculate the similarity between users, uses a frequent item set matrix and a user scoring matrix to calculate the similarity between items, and combines the user similarity and the item similarity to calculate the prediction score of the users for items which are not scored, thereby realizing Top-K recommendation. Compared with a traditional collaborative filtering recommendation algorithm based on a user and a collaborative filtering recommendation algorithm based on a project, the method can effectively avoid the cold start problem and the data sparsity problem, and has better recommendation quality.

Description

Technical field: [0001] The invention relates to a collaborative filtering recommendation method, in particular to a collaborative filtering recommendation method based on user clustering and item association analysis, which belongs to the technical field of computer data mining and information processing. [0002] technical background: [0003] With the rapid development of e-commerce, the types and quantities of goods provided by e-commerce platforms have increased dramatically, and the era of product information overload has come. In the face of massive product information, users with clear needs can locate the products they want to buy through the search function provided by the e-commerce platform. However, when user needs are uncertain or ambiguous, and it is difficult to search and locate through keywords, how to help users quickly find the products they are interested in is extremely important. The recommendation system emerged as the times require, as an effective i...

AI Technical Summary

Problems solved by technology

However, these two collaborative filtering recommendation algorithms and most of the improved algorithms based on these two algorithms have the problems of cold start, data sparseness and low recommendation accuracy.

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