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Personalized recommendation algorithm based on integrated regression

A recommendation algorithm and basic technology, applied in the field of information processing, can solve the problems of reducing the recommendation accuracy rate and low generalization ability, and achieve the effect of improving the recommendation accuracy rate, ensuring the maximization, and improving the generalization ability.

Pending Publication Date: 2019-10-01
四川金蜜信息技术有限公司
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Problems solved by technology

However, the existing recommendation algorithms are basically only improved and optimized for a certain influencing factor, and their generalization ability is low; at the same time, there is still a certain correlation between the weak learners in the existing ensemble regression algorithm, which is very important for The generated strong learner will have a negative impact, which greatly reduces the accuracy of the recommendation

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  • Personalized recommendation algorithm based on integrated regression
  • Personalized recommendation algorithm based on integrated regression
  • Personalized recommendation algorithm based on integrated regression

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Embodiment

[0040] The personalized recommendation algorithm based on integrated regression of the present invention comprises the following steps:

[0041] Step 1: Obtain the user data set, classify each user in the user data set as a sample, traverse the sample list, record the total number N of samples, and assign an initial weight d to each sample 0 , At the same time, obtain the basic information of the sample, which includes the number of items of the sample, the item number, the predicted rating value of the sample, and the user rating value of the sample item; set the number of algorithm iterations t, where t is 1~T; set the boundary parameters of the weak learner Sample weight parameter v, sample error threshold l, number of user-item pairs M, strong learner error threshold The real rating information y(x i ), the user rating y of the item corresponding to the user il , the sample slack variable δ i . The above parameters are the hyperparameters of the algorithm, which ar...

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Abstract

The invention discloses a personalized recommendation algorithm based on integrated regression, which is characterized by comprising the following steps of: 1, acquiring a user data set, classifying each user in the user data set into a sample, traversing the sample list, recording the total number N of samples, and assigning an initial weight d0 to each sample; and 2, integrating a plurality of basic recommendation algorithms to form a weak learner selection pool, and generating a basic recommendation algorithm list k. According to the invention, a plurality of different basic recommendationalgorithms are integrated to form the strong learner containing various different influence factors to solve the same problem, so that the generalization ability of the recommendation system is improved, and the final recommendation accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of information processing, and specifically provides an integrated regression-based personalized recommendation algorithm. Background technique [0002] The recommendation system is based on the user's interest characteristics and purchase behavior, and recommends information and products that the user is interested in to the user. The factors that affect the recommendation accuracy of the recommendation system are diversified. Since the emergence of the collaborative filtering recommendation (CF recommendation) algorithm, many scholars have devoted a lot of energy to the various platforms used by the algorithm and the outstanding problems in the algorithm. Algorithms are improved and optimized. However, the existing recommendation algorithms are basically only improved and optimized for a certain influencing factor, and their generalization ability is low; at the same time, there is still a certain correla...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/9536G06Q30/06
CPCG06F16/9535G06F16/9536G06Q30/0631
Inventor 琚生根李思骏周志钢李磊胡思才
Owner 四川金蜜信息技术有限公司
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