Model optimization method based on decision tree and recommendation method
A recommendation method and decision tree technology, applied in character and pattern recognition, resources, instruments, etc., can solve problems such as changes in user hobbies, time changes, and failure to consider, so as to avoid information islands, improve regulation and Coordination ability, the effect of improving the efficiency of information management
Pending Publication Date: 2021-08-31
WENZHOU UNIVERSITY
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However, there are many traditional recommendation algorithms, but they all have certain deficiencies. For example, the recommendation algorithm based on demographics is based on the basic information of system users (age, gender, interest, etc.). , to recommend the products that similar users like to the current users, without considering the differences in personal preferences, educational level and other factors, and also without considering the changes in user hobbies caused by time changes, space, and status. There are currently many recommendation models. Different recommendation models have different abilities to solve problems. It is still a challenging problem to choose which recommendation model to solve the feature combination problem of current products to further improve the accuracy of personalized content recommendation.
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[0023] In order to make the purpose, technical solution and advantages of the technical solution of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of specific embodiments of the present invention. The same reference numerals in the figures represent the same parts. It should be noted that the described embodiments are some of the embodiments of the present invention, but not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0024] The invention provides a model optimization method based on a decision tree and a recommendation method that can improve the prediction accuracy of recommended content and make the selection of data models faster and more effi...
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The invention provides a model optimization method based on a decision tree and a recommendation method, and the method comprises the steps: step 1, configuring at least two to-be-recommended data information sets for an information processing system, and building a corresponding evaluation index feature set; step 2, acquiring at least one piece of sample data, wherein each piece of sample data in the at least one piece of sample data comprises a selection label of the data information set and an evaluation index feature set; step 3, calculating the weight of the feature subset according to the average variation of Gini indexes when the feature attribute of the feature subset is used as a split node in each decision tree; step 4, calculating the similarity between the recommended user and each user under the feature subset Ui, and predicting the score of the recommended user on the data information set Oi under the feature subset Ui; and step 5, generating a recommendation result. According to the method, the prediction accuracy of the recommendation content can be improved, and the selection of a data model is quicker and more efficient.
Description
technical field [0001] The invention relates to the technical field of content recommendation, in particular to a model optimization method based on a decision tree and a recommendation method. Background technique [0002] The development of Internet technology has led to the rapid growth and expansion of information. How to quickly and effectively screen information, so as to accurately recommend personalized content suitable for users, such as commodities, advertisements, news and APP, etc., to users. It is an important issue that needs to be solved in the future development of the information service industry. However, there are many traditional recommendation algorithms, but they all have certain deficiencies. For example, the recommendation algorithm based on demographics is based on the basic information of system users (age, gender, interest, etc.). , to recommend the products that similar users like to the current users, without considering the differences in perso...
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IPC IPC(8): G06F16/9535G06K9/62G06Q10/06G06Q30/06
CPCG06F16/9535G06Q10/06393G06Q30/0631G06F18/24323
Inventor徐曰旺张笑钦王文哲刘丽颖
OwnerWENZHOU UNIVERSITY