Recommendation method and device, terminal equipment and computer storage medium

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...

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 accuracy of the shopping content through manual review; in addition, there is also the establishment of associations between scenes, etc., which need to be done manually intervene

[0004] To sum up, in the process of realizing scene-based shopping, a lot of labor costs are required to intervene, and each link requires manual evaluation or intervention, resulting in a large labor cost when realizing scene-based shopping, and it cannot be automated and large-scale, which greatly affects the Limit the promotion and application of scene-based shopping

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