Recommendation information generation method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides a recommendation information generation method and device, electronic equipment and a readable storage medium. The method comprises the following steps: according to an association relationship among user identifiers, object identifiers and situation attribute parameters, obtaining user identifiers; determining a first node comprising a context attribute parameter and a second node comprising a user identifier, and a plurality of paths including a third node of the object identification, according to each sub-parameter value included in the first node on the plurality of paths and a third node, grouping the plurality of paths; obtaining multiple sets of paths, according to the respective path attribute values of the multiple groups of paths; generating recommendation information of the to-be-recommended object, by adoption of the method provided by the embodiment of the invention, the process of generating the to-be-recommended information is combined with the situation attribute parameters and the path attribute values in the historical data of the field to which the recommendation information belongs, so that the recommendation informationfor the to-be-recommended object is richer and has reference, and more surprise and attraction can be brought to the user.

Description

technical field [0001] The embodiments of the present application relate to the technical field of data processing, and in particular to a method, device, electronic device, and readable storage medium for generating recommendation information. Background technique [0002] With the development of Internet technology, users frequently choose items online. Usually, the system will recommend an item to the user to help them choose. While recommending items, the system aims to enhance the user's trust in the recommended items and enhance the user's confidence in the recommended items. The experience will also give the reason for recommending the item, that is, the recommendation information. The existing recommendation information generation methods can be roughly divided into the following categories: [0003] Based on user reviews. High-quality review texts are mined from a large number of user reviews on the item, and then recommended to users. For example, "Tom Yum Kung ...

AI Technical Summary

Problems solved by technology

However, in the recommendation reason scenario, a single user comment often has limited information, and the sense of surprise and highlight is not prominent enough.

Based on social relations, based on user similarity and based on item similarity, the problem with these three types of recommendation information is that the description is too simple, which often brings users a sense of fatigue

[0008] Therefore, the recommendation information generation method in the prior art needs to be improved

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