Cloud computing technology based recommendation system implementation method

Abstract

The invention belongs to the field of a cloud computing technology and provides a CCBRS (Cloud Computing Based Recommendation System) based on comparative research on the traditional recommendation algorithm. Different recommendation strategies can be adopted according to different recommendation requirements. Multiple recommendation algorithms under the conditions of one-machine environment, pseudo-distributed platform and distributed platform are tested and analyzed combined with the CCBRS to achieve selection of the recommendation strategies under different conditions. According to the CCBRS based on the comparative research on the traditional recommendation algorithm, the different recommendation strategies can be adopted according to the different recommendation requirements and accordingly the versatility and the extendibility are good and a good support can be provided for large-scale data processing.

Description

technical field [0001] The invention relates to an implementation method of a recommendation system, in particular to a recommendation system based on cloud computing technology, and the invention belongs to the technical field of computer networks. Background technique [0002] The goal of a personalized recommendation system (referred to as a recommendation system) is to generate and provide meaningful recommendation information to a group of users who may be interested in products or items (items). Compared with traditional information system tools and technologies such as databases and search engines, research on recommendation systems started relatively late, and it became a relatively independent research field around the mid-1990s. In the past few years, the research and application of recommender systems have made great progress. In recent years, with the rapid development of Amazon, Yahoo, Google, YouTube, Netflix, IMDb and other popular websites and the new forms ...

AI Technical Summary

Problems solved by technology

In general, there are still some problems in the existing recommendation systems, such as: recommendation systems often need custom development and are not flexible enough, most of them fail to fully consider business strategies, and it is difficult to adopt different recommendation strategies according to changes in recommendation requirements, etc.

At the same time, with the further expansion of the scale of e-commerce systems, it becomes more and more difficult to provide real-time recommendation services for tens of thousands of users on large-scale datasets.

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