Method and device for recommending related items

Abstract

The embodiment of the invention provides a method and a device for recommending related items. The method for recommending related items comprises the following steps: constructing a relation graph of items, which takes a specific item corresponding to a queried key word as a vertex, according to a total relation graph of the items; determining a strong relation graph of the items according to the relation graph of the items; determining a candidate recommendation list which corresponds to the queried key word according to the strong relation graph of the items; and selecting a predetermined number of related items from the candidate recommendation list and recommending the related items to the user. Due to the adoption of the method and the device for recommending related items, the problem on maximum item relevance is effectively solved, and the product really needed by the user can be selected according to the current need of the target user and recommended to the target user.

Description

technical field [0001] The invention relates to the network field, in particular to a related item recommendation method and device. Background technique [0002] With the exponential growth of network information, personalized recommendation systems are widely used in TV program recommendation and e-commerce industries, automatically recommending program information and commodity resources of interest to users, which alleviates the problem of "information overload" to a certain extent . These recommended products are generally called items. [0003] In recent years, the technical solutions of personalized recommendation services have focused on the combined recommendation method that comprehensively utilizes various mature recommendation algorithms. However, how to provide users with flexible, comprehensive and high-quality relevant recommendation results based on the user's current favorite products has always is a hot topic of discussion, we call this problem the item m...

AI Technical Summary

Problems solved by technology

[0004] In the process of realizing the present invention, the inventor found that the existing relevant recommendation scheme has certain limitations: firstly, this scheme fails to comprehensively consider the role of the user interest model, treats all users equally, and returns popular information that may not be liked by users; secondly, this scheme is difficult to mine hidden interests of users, it can only recommend those products that frequently appear together with a given product, and it is possible for users to be interested in products that are more hidden. products, it is difficult to give recommendations; finally, this scheme is difficult to scale, it can only give relevant recommendations for a given product, and cannot handle the case of multiple given products

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