Bi-LSTM-CRF model-based content-related advertisement putting method and system
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A technology of advertising delivery and models, applied in neural learning methods, biological neural network models, marketing, etc., can solve the problems that the model is difficult to achieve a good recognition effect, it is not easy to automatically obtain features, and the advertising recommendation cannot be placed accurately.
Active Publication Date: 2021-06-04
HARBIN INST OF TECH
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[0004] In view of the above problems, the present invention proposes a content-related advertisement delivery method and system based on the Bi-LSTM-CRF model to solve the problem of small-scale data sets based on The named entity recognition model of deep learning, because it is not easy to automatically obtain features, makes it difficult for the model to achieve a good recognition effect, which further leads to the problem of inaccurate delivery of advertising recommendations
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specific Embodiment 1
[0070] Verify the effectiveness of the method of the present invention.
[0071] The experiment uses the post data obtained from the second-hand community. Through manual labeling, the data set contains 19449 post data, and there are a total of 29859 commodity entities after labeling. The experiment is performed by a computer with 2 core CPU and 8G memory, using pytorch The framework implements the algorithm.
[0072] Divide the marked corpus into training set, verification set and test set according to the ratio of 8:1:1 for model training. To find the best parameter settings for the model, a parameter search method is employed. In this method, the word vector dimension is set between [200, 256, 300], the number of units in the LSTM layer is set between [64, 128], and the value of dropout is between [0.4, 0.5, 0.6]. The optimal parameter combination of the model obtained from the final test is shown in Table 6.
[0073] Table 6 Model optimal training parameter settings
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specific Embodiment 2
[0081] This embodiment proposes a content-related advertisement delivery system based on the Bi-LSTM-CRF model, such as Figure 5 As shown, the system includes:
[0082] The prediction model training unit 110 is used to input the obtained training data set containing the labeling of the commodity entity into the Bi-LSTM-CRF model for training to obtain the optimal prediction model;
[0083] Commodity word prediction unit 120, for inputting the data to be predicted including the commodity entity into the optimal prediction model, and obtaining the predicted commodity word;
[0084] Advertisement information matching unit 130, configured to match relevant advertisements according to commodity words, and obtain advertisement information with the highest matching degree;
[0085] The advertisement delivery unit 140 is configured to deliver advertisements carrying advertisement information.
[0086] Further, the Bi-LSTM-CRF model in the prediction model training unit 110 includes...
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Abstract
The invention discloses a Bi-LSTM-CRF model-based content-related advertisement putting method and system, belongs to the technical field of advertisement putting, and is used for solving the problem that a deep learning-based named entity recognition model for a small-scale data set is difficult to obtain characteristics automatically, so that the model is difficult to obtain a good recognition effect, and the problem that advertisement recommendation cannot be accurately put is further solved. According to the technical key points, the method comprises the following steps: inputting a training data set into a Bi-LSTM-CRF model for training, and obtaining an optimal prediction model; inputting to-be-predicted data into the optimal prediction model to obtain predicted commodity words; matching related advertisements according to the commodity words, and obtaining advertisement information with the highest matching degree; and putting the advertisement carrying the advertisement information. According to the method, the features of the commodity words are combined on the basis of the Bi-LSTM-CRF algorithm, the data are enhanced in a feature engineering mode, the data are made to have richer semantics, a system suitable for document commodity word extraction is constructed and used for content-related advertisement recommendation, and the precise advertisement putting effect is improved.
Description
technical field [0001] The invention relates to the technical field of advertisement delivery, in particular to a content-related advertisement delivery method and system based on a Bi-LSTM-CRF model. Background technique [0002] The second-hand community is a network platform for users to publish and browse posts. Users can post posts to express their demands for idle transfers or browse posts to see if they meet their purchase intentions. For the second-hand community, the main way of income is advertising. Since the advertising process of the second-hand community needs to be accurately placed, it is necessary to start from the content of the community to analyze the user's purchase intention and use this to place advertisements, that is, content-related advertisements. The main idea of contextual advertising is to deliver advertisements related to the content of the webpage while providing users with the content of the webpage. In the prior art, a method of keyword e...
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