Recommendation method and device, terminal equipment and computer storage medium

A recommendation method and clustering technology, applied in the field of data processing, can solve the problems of inability to automate large-scale, limited scenario-based shopping promotion and application, and high labor costs, so as to facilitate large-scale application, less manual intervention, and improve production. The effect of efficiency

Active Publication Date: 2020-08-11
ALIBABA GRP HLDG LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the existing methods for realizing the scene-based shopping experience need to determine each scene through exhaustive mining, and then determine the rationality of the scene through manual review; when determining the shopping content corresponding to the scene, it is necessary to first determine the category corresponding to the scene , and then determine the attributes and attribute values ​​​​corresponding to the products under the category to determine the shopping content that matches the scene, and confirm the accura

Method used

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  • Recommendation method and device, terminal equipment and computer storage medium
  • Recommendation method and device, terminal equipment and computer storage medium
  • Recommendation method and device, terminal equipment and computer storage medium

Examples

Experimental program
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Embodiment 1

[0026] refer to Figure 1a , shows a flowchart of steps of a recommendation method according to Embodiment 1 of the present invention.

[0027] The recommended method of this embodiment includes the following steps:

[0028] S102. Clustering several candidate recommendation objects according to the historical behavior data of the user, so as to obtain multiple clusters respectively corresponding to multiple scenarios.

[0029] In this embodiment, the user's historical behavior data is the data corresponding to the historical behavior corresponding to the user's operation on each candidate recommended object. Key words, according to the user's operation to determine the product selected by the user after searching, the order in which the user searches for each product, etc. The user historical behavior data may include user historical behavior data corresponding to multiple users, which is not limited in this embodiment.

[0030] Most users operate on candidate recommendation...

Embodiment 2

[0039] refer to figure 2 , shows a flowchart of steps of a recommendation method according to Embodiment 2 of the present invention.

[0040] The recommended method of this embodiment includes the following steps:

[0041]S202. Based on the attributes and categories of the several candidate recommended objects, cluster the candidate recommended objects to obtain multiple candidate entities.

[0042] In this embodiment, because the number of candidate recommendation objects may be huge, or because the user's behavior granularity for a single candidate recommendation object is relatively sparse, accurate clustering results cannot be obtained when clustering is performed directly based on candidate recommendation objects. Therefore, in order to improve the accuracy of the clustering result, this embodiment clusters the candidate recommendation objects according to the attributes and categories of the candidate recommendation objects, so as to obtain multiple candidate entities....

Embodiment 3

[0071] refer to Figure 3a , shows a flowchart of steps of a recommendation method according to Embodiment 3 of the present invention.

[0072] The recommended method of this embodiment includes the following steps:

[0073] S302. Cluster the several candidate recommendation objects according to the historical user behavior data, so as to obtain multiple clusters respectively corresponding to multiple scenarios.

[0074] In this embodiment, if the candidate recommendation object is a commodity, then as Figure 3b As shown, when performing clustering, the products can be clustered according to the user's historical user behavior data for the products and the product information of each product, and multiple clusters corresponding to multiple scenarios can be obtained.

[0075] S304. Determine a class of applicable scenario identifiers corresponding to each cluster according to the applicable scenario identifiers of the candidate recommendation objects included in each of the ...

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Abstract

An embodiment of the invention provides a recommendation method, a recommendation device, terminal equipment and a computer storage medium. The recommendation method comprises the steps of: clusteringa plurality of candidate recommendation objects according to historical behavior data of a user to obtain a plurality of clustering clusters corresponding to a plurality of scenes; according to the category corresponding to each clustering cluster, determining an association relationship among the clustering clusters to establish a scene map of the candidate recommendation object; and determiningan effective recommendation object recommended to a target user from the candidate recommendation objects according to the established scene map. According to the recommendation method and the recommendation device provided by the embodiment of the invention, less manual intervention is required when a knowledge graph is established, and the production efficiency is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of data processing, and in particular to a recommendation method, device, terminal equipment, and computer storage medium. Background technique [0002] With the advent of the intelligent age, users have higher and higher requirements for shopping experience. Therefore, existing e-commerce companies can not only recommend corresponding products to users, but also provide users with scene-based shopping experience on this basis. The best shopping experience is to recommend other content in this scene to the user based on the content that the user has already searched. For example, if a user searches for "beach shorts" and "beach shoes", it will recommend other shopping content in the "beach vacation" scenario, such as "swimming goggles" and "sunscreen". [0003] However, the existing methods for realizing the scene-based shopping experience need to determine each scene through exhau...

Claims

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Application Information

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IPC IPC(8): G06F16/906G06F16/9536G06Q30/06
CPCG06F16/906G06F16/9536G06Q30/0631
Inventor 李朝潘旭明邹朋成
Owner ALIBABA GRP HLDG LTD
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