Financial product recommendation method and system, storage medium and equipment
A recommendation method and product technology, applied in neural learning methods, data processing applications, website content management, etc., can solve problems such as the impact of clustering results, inaccurate initialization, and failure to consider the local similarity of financial data samples, etc., to achieve effective improvement The effect of improving the accuracy of clustering
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Embodiment 1
[0043] Such as figure 2 As shown, this embodiment provides a method for recommending wealth management products, which specifically includes the following steps:
[0044] S101: Obtain wealth management user data.
[0045] Such as figure 1 As shown, the obtained wealth management user data Raw Data (RD), wherein, the number is N, and the data attribute is D, such as education, income, age, occupation, geographical distribution, etc.
[0046] S102: Based on the wealth management user data and the trained self-encoding neural network model, obtain the recommended wealth management product type;
[0047] Among them, the training process of the self-encoding neural network model is:
[0048] Using financial user data to pre-train the self-encoding neural network model;
[0049] Splicing wealth management user data and several nearest neighbor data to form training data;
[0050] Taking the parameters in the pre-trained self-encoding neural network model as initial values, the...
Embodiment 2
[0080] Such as Figure 4 As shown, the present embodiment provides a wealth management product recommendation system, which specifically includes the following modules:
[0081] A wealth management user data acquisition module, which is used to acquire wealth management user data;
[0082] Recommend a wealth management product recommendation module, which is used to obtain the type of recommended wealth management products based on the wealth management user data and the trained self-encoded neural network model;
[0083] Among them, the training process of the self-encoding neural network model is:
[0084] Using financial user data to pre-train the self-encoding neural network model;
[0085] Splicing wealth management user data and several nearest neighbor data to form training data;
[0086] Taking the parameters in the pre-trained self-encoding neural network model as initial values, the training data is used to continue training the self-encoding neural network model ...
Embodiment 3
[0089] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the method for recommending a wealth management product as described in the first embodiment above are implemented.
[0090] Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM) or a random access memory (Random Access Memory, RAM) and the like.
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Abstract
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Application Information
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