Product recommendation method and device

A recommendation method and product technology, applied in the field of data processing, can solve the problems of sparse data, sparse user behavior information, no user data, etc., achieve the effect of improving accuracy, alleviating transaction data sparseness and cold start problems, and accurately recommending services

Inactive Publication Date: 2018-10-26
ALIBABA GRP HLDG LTD
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Problems solved by technology

For example, in the recommendation of financial wealth management products, due to the nature of the financial wealth management industry itself, such as large transaction volume and low frequency, user behavior information is scarce, and there is not a large amount of user data for prod...

Method used

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  • Product recommendation method and device
  • Product recommendation method and device
  • Product recommendation method and device

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Embodiment Construction

[0033] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will describe the technical solutions in one or more embodiments of this specification in conjunction with the drawings in one or more embodiments of this specification The technical solution is clearly and completely described, and obviously, the described embodiments are only a part of the embodiments in this specification, rather than all the embodiments. Based on one or more embodiments in this specification, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

[0034] One or more embodiments of this specification provide a product recommendation method when data is sparse. The description of this method takes the recommendation of financial wealth management products as an example, but it is understandable t...

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Abstract

The embodiment of the invention provides a product recommendation method and device. The method is used for determining whether to recommend a to-be-recommended product to a target user. The method comprises the steps that multi-field information associated with the target user is acquired, wherein the information comprises purchase data of the target user in the product field of the to-be-recommended product and purchase data in other product fields; a user feature matrix of the target user is constructed according to the multi-field information; for one to-be-recommended product, a user feature matrix of multiple users who purchase the to-be-recommended product is acquired, and a product feature matrix of the to-be-recommended product is obtained based on feature values in the matrix; the user feature matrix and the product feature matrix are input into a machine learning model to obtain user preference vectors and product preference vectors; a selection assessment value between theto-be-recommended product and the target user is obtained according to the user preference vectors and the product preference vectors; and when the selection assessment value is greater than a predetermined recommendation threshold, it is determined that the to-be-recommended product is recommended to the target user.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular to a product recommendation method and device. Background technique [0002] In the field of product recommendation, cold starts and data sparsity are common problems. Cold start refers to product recommendation without a large amount of user data support; data sparseness means that the items that interact with users are only the tip of the iceberg of the overall items, resulting in extremely sparse data in the user-item scoring matrix. For example, in the recommendation of financial wealth management products, due to the nature of the financial wealth management industry itself, such as large transaction volume and low frequency, user behavior information is scarce, and there is not a large amount of user data for product recommendation, resulting in cold start problems; Moreover, users' purchases of financial wealth management products only account for a smal...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q40/06G06F17/30
CPCG06Q30/0255G06Q30/0269G06Q40/06
Inventor 张连彬
Owner ALIBABA GRP HLDG LTD
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