Financial product content recommendation method and system and computer readable storage medium
A financial product and content recommendation technology, applied in finance, computing, neural learning methods, etc., to achieve high recommendation adoption rate and accurate and complete customer information
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Embodiment 1
[0052] This embodiment provides a method for recommending financial product content, which can push product content to users more accurately and precisely; it includes the following steps:
[0053] Step 1: Obtain the user preference data information identified by the mobile phone user identifier from multiple different data sources;
[0054] Step 2: Input the user preference information into the convolutional neural network module and generate a content catalog of financial products to be recommended;
[0055] Step 3: Retrieving financial product information related to the content catalog of financial products to be recommended from the financial product information database according to the content information in the content catalog of financial products to be recommended;
[0056] Step 4: Visually display the retrieved financial product information on the user terminal.
[0057]Preferably, in one of the preferred technical solutions of this embodiment, in the step 1, the pl...
Embodiment 2
[0060] In this embodiment, on the basis of embodiment 1, in the step 2, the content recommendation model is a recommendation calculation unit, including: a deep learning algorithm and a delivery rule.
[0061] Preferably, in one of the preferred technical solutions of this embodiment, in the step 2, the convolutional neural network includes: a content recommendation model and a method recommendation model.
[0062] Preferably, in one of the preferred technical solutions of this embodiment, the recommendation result of the method recommendation model includes: at least one of products, services, and advertising activities.
[0063] Preferably, in one of the preferred technical solutions of this embodiment, if the recommendation result includes multiple recommended contents, the multiple recommended contents are sorted and recommended to corresponding customers according to corresponding recommendation methods.
[0064] It should be noted that the above-mentioned deep learning a...
Embodiment 3
[0075] This embodiment provides a method for recommending financial product content, which can push product content to users more accurately and precisely; it includes the following steps:
[0076] Step 1: Collect user preference data information and user behavior information from multiple different data sources, and generate a user information set based on the user preference data information and user behavior information; the user preference data information includes: clothing, beauty makeup, At least one of sports, technology, fitness, food, finance, lending, real estate, leasing, history and geography; the user behavior information includes: including: click, favorite, share, like, follow, play, like, dislike At least one of like and report;
[0077] Step 2: Construct a content recommendation model based on the user information set, and train a convolutional neural network;
[0078] Step 3: Push recommended content to customers through the convolutional neural network.
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