Friend recommendation method, system and storage medium introducing attention mechanism

Abstract

The invention discloses a method for recommending friends by introducing an attention mechanism, which comprises the following steps: performing feature extraction on a user pair to obtain multi-dimensional feature information, wherein the user pair includes a target user and a candidate user; Perform dynamic weighting to obtain output results; recommend friends based on output results. The invention provides a friend recommendation method introducing an attention mechanism to improve the practicability and effectiveness of the recommendation function. As a friend recommendation method, system and storage medium introducing an attention mechanism, the present invention can be widely applied to Internet technology.

Description

technical field [0001] The invention relates to Internet technology, in particular to a friend recommendation method, system and storage medium that introduce an attention mechanism. Background technique [0002] In recent years, with the rapid development of Internet technology, online social networking platforms have been fully popularized, and users can conduct convenient information exchange and information sharing through social networking platforms. However, as the number of users continues to increase, more and more information can be obtained, and a large amount of redundant information will affect the use of users. Therefore, recommender systems have emerged, which can discover and recommend content to users from massive amounts of information to help alleviate the problem of information overload, and have been extensively studied today. [0003] Traditional content-based recommendation methods only make recommendations by learning simple linear relationships betwe...

AI Technical Summary

Problems solved by technology

However, in actual situations, target users usually do not pay attention to all the features of the recommended items. For example, in the friend recommendation task in social networks, different users will choose friends according to different characteristics, and some users choose users with similar research fields. become friends, and most of the friends of some users are from the same school, so the existing friend recommendation methods cannot accurately obtain the different preferences of different users, and cannot give reasonable recommendation results according to the user's attention to different characteristics. Poor functionality and effectiveness

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