Dynamic recommendation method based on training set optimization for recommendation system

Abstract

The invention discloses a dynamic recommendation method based on training set optimization for a recommendation system, which specifically includes: (1) establishing a preliminary recommendation portion: generating an original recommendation model according to original user grading data; (2) performing AdaBoost trainings: utilizing the original recommendation model as a classifying and judging basis to classify the data and adjust learning times of samples by means of multiple iterative learning training data; (3) screening incorrect samples: data of selected difficult samples are removed as the incorrect samples after multiple AdaBoost trainings so as to construct a new training data collection; (4) reconstructing a recommendation model: combining training results to regenerate the recommendation model based on the new training data; and (5) generating recommendation results: utilizing the new recommendation model to generate the recommendation results. The method is capable of removing the data without referential meaning in recommended service by the aid of great relevance of original training set data in content, so that validity of the training data and precision of the final recommendation model are improved.

Description

technical field [0001] The invention relates to the technical field of user recommendation systems, in particular to a dynamic recommendation method of a recommendation system based on training set optimization. Background technique [0002] Personalized recommendation service is user-centered, based on understanding user preferences, and provides users with customized personalized information presentation services. It is also an effective way to solve the problem of extracting the information users need from massive Internet resources. Compared with the ordinary service model, the personalized recommendation service has the following characteristics: First, the personalized recommendation service can save users from the dilemma of information overload, so that users can have the opportunity to enjoy truly rich and colorful, convenient and appropriate humanized network information services, greatly improving user experience and satisfaction; secondly, personalized recommenda...

AI Technical Summary

Problems solved by technology

The large scale of the data set is difficult to avoid the existence of unreasonable data in the data collection, such as the phenomenon of wrong ratings by users or substituting ratings by non-users.

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