Constraint condition-based random forest recommendation algorithm

Abstract

The invention discloses a constraint condition-based random forest recommendation algorithm. The algorithm comprises the steps of generating a binary decision tree according to a data set in a given initial training set; randomly selecting features to form a random forest; measuring time efficiency of a decision tree algorithm; measuring efficiency of a random forest algorithm; and improving the random forest algorithm. Compared with other recommendation algorithms, the random forest recommendation algorithm has the advantages that the superiority of the random forest classification algorithm is fully understood, and the random forest classification algorithm is autonomously realized, so that the understanding of a recommendation system is deepened; and finally, key points causing algorithm deficiencies are researched from algorithm construction, and the algorithm is improved, thereby enabling the algorithm to have better efficiency.

Description

technical field [0001] The invention relates to the technical field of data mining and recommendation algorithms, in particular to an improved random forest recommendation algorithm. Background technique [0002] With the advancement of science and technology, people have gradually entered the Internet of Things era from the Internet information age. The rise of social networking and service industry websites has made the amount of information grow rapidly at an exponential rate. The amount of data on the Internet has long been different from what it used to be, and the sources of information are still increasing. For the challenge faced by the Internet - information overload, the recommendation system has set off an upsurge in the field of machine learning. The recommendation system has extremely high application value and has been widely used in many fields. For example, with the rapid development of e-commerce in recent years, online shopping has become the mainstream, ...

AI Technical Summary

Problems solved by technology

The decision tree algorithm has certain shortcomings. Due to the depth-first search, the algorithm is limited by the memory size and it is difficult to handle large training sets.

Various improved algorithms (discretization, sampling) to deal with large data sets or continuous quantities not only increase the overhead of classification algorithms, but also reduce the accuracy of classification. It is difficult to predict continuous fields. When there are too many categories, errors It may increase faster. For time-ordered data, a lot of preprocessing work is required.

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