Collaborative filtering scheme recommendation method fusing local similarity and global similarity

Abstract

The invention discloses a collaborative filtering scheme recommendation method fusing local similarity and global similarity, and belongs to the technical field of scheme recommendation. Based on a collaborative filtering algorithm, the collaborative filtering scheme recommendation method makes a breakthrough in the aspect of similarity calculation, including: firstly, calculating global similarity by adopting all user project scores, effectively utilizing the data information, being able to solve data sparsity; and secondly, dividing the scores into positive scores, negative scores and combination of the two scores by the aid of common score information of the users, calculating the local similarity, so that common information between the users can be fully and effectively utilized, and the two kinds of similarity are fused, and prediction accuracy can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of recommendation schemes, and relates to a method for recommending schemes in collaborative filtering, in particular to a method for recommending schemes based on user-based collaborative filtering. Background technique [0002] The fourth-generation industrial revolution has come quietly, and now we are in the era of data explosion. With the advent of the era of big data, people are faced with many difficult problems in choosing items. How to find their goals efficiently and accurately has become a practical problem. Therefore, The recommendation system came into being. Recommender systems arise from the very simple phenomenon that people often listen to other people's opinions when making their own choices. The recommendation system plays a very important role in many websites, such as Ali’s product recommendation, Tencent’s friend recommendation, Netease Cloud’s song list recommendation, Baidu’s search ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the existing data sparseness and low recommendation accuracy problems in the collaborative filtering algorithm, the present invention proposes a collaborative filtering recommendation scheme fusion local similarity and global similarity method, namely (Combine-local-and-globalsimilarities measures, CLAG_CF) method, which can effectively solve the problem of data sparsity and improve the recommendation accuracy

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