Cloud-computing-based smart recommendation method and system

Abstract

The invention relates to a cloud-computing-based smart recommendation method that is implemented on a Hadoop cloud computing platform based on collaborative filtering algorithm of items or articles. The method comprises the steps of S1, establishing a concurrent matrix of articles; S2, establishing a user's score matrix for the articles; S3, calculating recommendation results through the matrixes; S4, storing the recommendation results; S5, recommending the results. The invention also discloses a cloud-computing-based smart recommendation system. The Cloud-computing-based smart recommendation method and system have the advantages that the high complexity problem of an algorithm can be solved by using a cloud computing concurrent programming model, and complex operation is decomposed into simple task executions; additionally, by cloud computing, it is possible to provide a large-scale data storage system, and limits on hardware of a single computer are surpassed; the recommendation algorithm and the Hadoop cloud computing platform are integrated effectively, algorithm concurrence is achieved through the MapReduce distributed programming model, and operating efficiency of the algorithm is improved.

Description

technical field [0001] The invention relates to the technical field of resource recommendation, in particular to an intelligent recommendation method and system based on cloud computing. Background technique [0002] With the rapid development of Internet technology and information technology, many aspects of people's lives have been affected by Internet technology. When we use Internet products, data is always generated continuously. The daily data volume of some large enterprises has reached hundreds of terabytes, and the total amount of data storage has already exceeded 1000PB. With such an increase in information, although most irrelevant commodities can be filtered through search technology, there are a large number of commodities that users are potentially interested in in the huge database, so users need a service to filter effective information. The demand for such services directly leads to the generation of recommendation systems. The recommendation system can mi...

AI Technical Summary

Problems solved by technology

[0004] The disadvantage of the existing technology is that the complexity of the traditional recommendation algorithm is very high, resulting in low computing efficiency, and conventional data mining is often based on large-scale data collections, complex calculations and large-scale data are limited by computer hardware Limitations do not allow for ideal operating results

Traditional stand-alone serial computing can no longer meet the processing requirements of high complexity and big data

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