Characteristic recommendation method and device

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.

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...

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Problems solved by technology

[0005] However, all pairwise interaction features are simulated in FM, but no effective feature combination is selected. In reality, some interaction features may be invalid. In the FM model, the weights of all interaction features are shared through the low-rank Vector inner product to obtain, if an interaction feature is invalid, it will lead to inaccurate parameter estimation and final result prediction

[0006] In addition, the random split tree algorithm does not discuss the selection of combined features. When there are dozens of discrete features, the random split tree algorithm is not very effective.

[0007] To sum up, the existing technology has the problem that the combined features cannot be effectively selected, and the effectiveness of the recommended text features is low.

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