Model generation method and device as well as recommendation method and device

Abstract

The embodiment of the invention provides a model generation method, a model generation device, a recommendation method and a recommendation device. The model generation method in the embodiment of the invention comprises the following steps: acquiring a document content feature vector of each entity in a mapping knowledge domain, logical relationship feature vectors among the entities, a user behavior relationship feature vector of each entity and at least one of feature vectors of each entity, then performing machine learning according to the document content feature vectors, the logical relationship feature vectors, the user behavior relationship feature vectors and the at least one of the feature vectors, and generating a deep fusion model. Therefore, according to the technical scheme provided by the embodiment of the invention, the deep fusion model can be generated by integrating various relationships among the entities and can be used for acquiring the surprise degree among the entities, so that the entities can be recommended to users based on the surprise degree, the search recommendation requirements of the users are met, and the click rate of the recommended entities is improved.

Description

【Technical field】 [0001] The present invention relates to the field of search technology, in particular to a model generation method and device, and a recommendation method and device. 【Background technique】 [0002] At present, when the search recommendation is performed, it is based on satisfying the user's main search demand, and stimulating the potential demand of the user by providing the user with other potentially interesting content related to the query word. For example, please refer to figure 1 , which is the first example of search recommendation based on knowledge graphs in the prior art. As shown in the figure, when a user queries "Princeton University", it can be recommended in the non-search result area of ​​the search result page figure 1 Notable alumni of Princeton University shown, this is a recommended entity that is very relevant to the query term "Princeton University". [0003] However, in the prior art, when searching and recommending based on knowle...

AI Technical Summary

Problems solved by technology

[0003] However, in the prior art, when searching and recommending based on knowledge graphs, the recommended entities are often well-known and cannot arouse user interest.

Therefore, this search recommendation method cannot meet the user's search and recommendation needs, resulting in a relatively low click-through rate for recommended entities.

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