Dynamic recommendation method based on training set optimization for recommendation system

A recommendation system and recommendation method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as unreasonable data, wrong ratings, etc., and achieve the effect of improving recommendation accuracy

Inactive Publication Date: 2012-06-20
BEIHANG UNIV
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  • Summary
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  • Claims
  • Application Information

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.

Method used

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  • Dynamic recommendation method based on training set optimization for recommendation system
  • Dynamic recommendation method based on training set optimization for recommendation system
  • Dynamic recommendation method based on training set optimization for recommendation system

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Embodiment Construction

[0030] Embodiments of the present invention are now described in conjunction with the accompanying drawings.

[0031] like figure 2 As shown, the present invention includes four main steps: building recommendation model, AdaBoost training, screening error samples and reconstructing recommendation model.

[0032] Step (1) Establish a recommendation model: read the original user rating data and test data, and determine the dimensions of the user feature vector and item feature vector according to the largest user number UserID and item number ItemID in the two data, based on the normalization matrix factor The modeling method in the recommendation model is decomposed by formula, and the user feature vector and item feature vector are newly created and randomly initialized;

[0033] Step (2) AdaBoost training stage: use the recommendation model generated in step (1) as the basis for classification judgment to construct a classifier, and determine the classification of data acco...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 欧阳元新蒋祥涛罗建辉熊璋
Owner BEIHANG UNIV
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