Improved commodity recommendation method based on multi-type implicit feedback

A product recommendation and implicit feedback technology, applied in the Internet field, can solve the problems of low user activity and low product recommendation accuracy

Active Publication Date: 2019-10-18
TIANJIN UNIV
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  • Abstract
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Problems solved by technology

[0006] The present invention proposes an improved product recommendation method based on multi-type implicit feedback to at least partially solve the existing methods due to low user activity, data sparsity and asymmetry, and diversification of user operation behaviors. The resulting problem of low product recommendation accuracy

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  • Improved commodity recommendation method based on multi-type implicit feedback
  • Improved commodity recommendation method based on multi-type implicit feedback
  • Improved commodity recommendation method based on multi-type implicit feedback

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Embodiment Construction

[0083] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0084] The present invention provides an improved product recommendation method based on multi-type implicit feedback, figure 1 is a schematic diagram of an improved commodity recommendation method based on multi-type implicit feedback in an embodiment of the present invention, as shown in figure 1 As shown, it includes: the preparation stage, obtaining and processing target user and product set data from the set e-commerce website; the learning stage, using the target user and product set data processed in the preparation stage to train the product recommendation algorithm and generate a ranking model ; In the sorting stage, use the sorting model to sort the recommended products and output a recommended product list acco...

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Abstract

The invention discloses an improved commodity recommendation method based on multi-type implicit feedback, and the method comprises the steps: a preparation stage: obtaining a target user and commodity set data from a set e-commerce website, and processing the target user and commodity set data; a learning stage: training a commodity recommendation algorithm by using the target user and commodityset data processed in the preparation stage, and generating a sorting model; and a sorting stage: sorting the recommended commodities by using the sorting model according to the target user and the commodity set data, and outputting a commodity recommendation list.

Description

technical field [0001] The invention relates to the Internet field, in particular to an improved product recommendation method based on multi-type implicit feedback. Background technique [0002] In the era of big data, people need to extract the information they need in a timely and accurate manner. "Personal customization" is particularly important. How to quickly obtain effective information from the huge and complex data, save users' time, and improve users' shopping satisfaction It is one of the important problems that the e-commerce platform urgently needs to solve, and the personalized recommendation system can well meet people's needs because of its good interactivity. [0003] The research on traditional recommendation algorithms mainly focuses on the data of explicit feedback, using the information provided by users for modeling processing. However, with the expansion of the data scale, the problems caused by the limited information that users can provide become mo...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06F16/9535
CPCG06Q30/0255G06Q30/0269G06F16/9535
Inventor 刘璇张蕾
Owner TIANJIN UNIV
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