Characteristic recommendation method and device

A recommendation method and technology of combining features, applied in the Internet field, can solve the problems that the selection of combined features is not discussed, the effectiveness of recommended text features is low, and the combined features cannot be effectively selected, so as to solve the problem of time-consuming and labor-intensive and improve the effectiveness.
CN104615790AActive Publication Date: 2015-05-13BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
Publication Date
2015-05-13

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Abstract

The invention discloses a characteristic recommendation method and a characteristic recommendation device. The characteristic recommendation method comprises the following steps: according to an output textual characteristic estimation model, determining target values of textual characteristics in sample data, wherein the output textual characteristic estimation model is obtained according to the optimal combined characteristics selected from training data; according to the target values, sorting the textual characteristics in the sample data and according to a sequence of the target value from high to low, carrying out recommendation on the textual characteristics in the sample data. The characteristic recommendation method and the characteristic recommendation device can realize automatic selection of the effective combined characteristics, are time-saving and labor-saving, effectively solve the difficult problems of time waste and labor waste in the existing manual characteristic selecting process and can improve effectiveness of a recommendation system.
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Description

technical field

[0001] The present invention relates to the technical field of the Internet, in particular to a feature recommendation method and device. Background technique

[0002] In the prior art, the text recommendation system usually adopts the following methods when selecting features:

[0003] 1. Selection by Factorization Machines (hereinafter referred to as: FM), where FM is a generalized model, mainly used to model all pairwise interaction features, and the parameters of the interaction features are passed through the shared low-rank vector inner product get;

[0004] 2. Select by random split tree algorithm, specifically, use text information to separate the user item matrix into sub-matrices according to specific text values, and then perform matrix decomposition for each sub-matrix, and the final predicted value is the average of T generated decision tree predictions value.

[0005] However, all pairwise interaction features are simulated in FM, but no effe...

Claims

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