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Advertisement click rate prediction framework and algorithm based on user behaviours

A technology for advertising clicks and advertisements, applied in forecasting, marketing, computing, etc., can solve problems such as category imbalance, large amount of data in ad click log files, difficulty in modeling interest drift, etc., to improve feature expression and reduce Effects of feature redundancy and sparsity

Inactive Publication Date: 2018-11-16
SICHUAN UNIV
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Problems solved by technology

[0004] The current research has the following difficulties [10-12]: 1. Advertisement click log files have a large amount of data and grow rapidly; 2. Advertisement click log files contain a large number of category features with many values; 3. Advertisement click rate values ​​are small and It has a long-tail distribution, and at the same time, there is a problem of category imbalance; 4. It is difficult to model the phenomenon of interest drift based on certain assumptions

Method used

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  • Advertisement click rate prediction framework and algorithm based on user behaviours
  • Advertisement click rate prediction framework and algorithm based on user behaviours
  • Advertisement click rate prediction framework and algorithm based on user behaviours

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Embodiment Construction

[0062] The present invention will be further described below in conjunction with accompanying drawing:

[0063] Algorithm framework

[0064] The present invention first uses the down-sampling algorithm based on the K_Means model to solve the problem of category imbalance, then uses heuristic thinking to classify features, and then uses gradient boosting trees to perform feature combination on perceptual features, and finally combines the combined Features and rational features are input into the logistic regression model according to a certain weight to predict the click-through rate of advertisements. Algorithm framework of the present invention such as figure 1 shown.

[0065] Feature extraction:

[0066] The present invention performs feature extraction based on experimental data sets and actual business analysis, with the purpose of reducing feature redundancy and feature sparsity and improving feature expression. The main features are as follows:

[0067] Query relev...

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Abstract

The invention discloses an advertisement click rate prediction framework and algorithm based on user behaviours. ID characteristics and other characteristics are co-converted in different levels intomeaningful numerical characteristics; due to the characteristics, the characteristic sparsity and redundancy can be reduced; the characteristic expressiveness can be improved; simultaneously, to further improve the characteristic expressiveness, characteristic selection and characteristic combination are carried out by utilization of a GBDT model in the invention; high-dimensional characteristicsare processed by utilization of an LR model; finally, to solve a class imbalance problem, a down-sampling algorithm based on a K_Means model is provided in the invention; in an experimental process, characteristic extraction on original characteristics is carried out at first; then, characteristic classification is carried out by adoption of heuristic thinking; characteristic combination is carried out by inputting perceptual characteristics into the GBDT model; finally, rational characteristics and combination characteristics are input into the LR model with a certain weight, so that advertisement click rate prediction is carried out; and an experimental result shows that the algorithm in the invention is improved both on RMSE and R2 indexes.

Description

technical field [0001] The invention relates to an advertisement click-through rate prediction algorithm, in particular to a user behavior-based advertisement click-through rate prediction framework and algorithm. Background technique [0002] The rapid development of the Internet provides a broad platform for the advertising industry. Internet advertising [1-2] has the advantages of wide audience, strong interactivity, and real-time flexibility, which makes the advertising industry gradually lean towards it. Internet advertisements can utilize users' surfing behaviors, tap user interests, and achieve accurate advertising pushes, which not only improves user experience, but also brings economic benefits. The click rate prediction algorithm is one of the core algorithms of the advertising system. It is based on the session log to predict the probability of the user clicking on the advertisement when the user queries and the advertisement is given. [0003] Accurate advertis...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q30/02
CPCG06Q10/04G06Q30/0242
Inventor 琚生根孙界平李兴国王婧妍刘宁宁张芮金玉
Owner SICHUAN UNIV
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