Social activity recommendation method based on heterogeneous graph model

Abstract

The invention discloses a social activity recommendation method based on a heterogeneous graph model. The method comprises the steps that an offline social activity is recommended to a user based on the hobby and interest, social relation, position preference, historical behaviors, etc. of the user, and a user distribution scheme is adjusted adaptively according to the scale of the activity. The heterogeneous graph model is the core of the technology, and construction of the heterogeneous graph model mainly comprises the steps of influence factor discrimination, feature dimension reduction, heterogeneous graph node selection, node connection establishment and "virtual connection" establishment for hanging nodes. After the graph model is established, a recommendation problem is transformed into a node adjacency calculation problem in the graph model from a similarity calculation problem between the user and the activity. In a graph network, user nodes and activity nodes are connected directly or indirectly through edges, the nodes in close connection have higher relevancy, candidate activities are arranged according to a descending order according to the relevancy between the user nodes and all the activity nodes, and therefore K users with the highest values are selected to form a user recommendation list.

Description

technical field [0001] The invention belongs to the field of big data and artificial intelligence, and more specifically relates to a social activity recommendation method based on a heterogeneous graph model. Background technique [0002] In recent years, under the background of the continuous integration of the real world and cyber space, activity-based social network platforms have developed rapidly, such as Meetup, Plancast, Douban city activities, QQ group activities, etc. The establishment of these platforms has promoted the spread of social activities and provided a convenient communication space for activity initiators and platform users. However, with the popularization of social network platforms, the scale of the platform has expanded rapidly. According to statistics in 2012, Meetup has accumulated 16 million users, and the average number of monthly activities on the platform has reached 300,000. It has become a time-consuming and labor-intensive task for users t...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a social activity recommendation method based on a heterogeneous graph model. In the method, the processing of hanging nodes affects the recommendation results, there is an oversaturation phenomenon in the recommendation of popular social activities, and the technical problems of low efficiency when social activities are dynamically updated

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