Social information enhancement-based event social network recommendation algorithm

Abstract

The invention relates to a social information enhancement-based event social network recommendation algorithm. According to the algorithm, the cold-start recommendation problem of event recommendationis solved by trying to combine information of a conventional social network and an event social network. Firstly, by utilizing social information of an event organizer, an event organizer assessmentalgorithm is introduced, thereby enhancing the effect of the recommendation algorithm. Meanwhile, the call guide effect of event participants is calculated, and the influence effect of the participants on a user is quantized and serves as a factor of a recommendation event. Secondly, the influence effects of the organizer and the participants of the event, and information in various aspects such as time, place, contents, groups and the like of the event are integrated, and selection preferences of the user are found. Finally, in combination with a search sorting algorithm, a recommendation list is calculated for each user, thereby realizing personalized recommendation.

Description

technical field [0001] The invention relates to a social network recommendation algorithm based on social information enhancement, which belongs to the field of data mining. Background technique [0002] With the development of science and technology, social networks have gradually matured in recent years, which brings opportunities for research on data mining and analysis of social networks. The event social network is a new type of online social network on which users can create, promote and share social activities. For example, the very popular Meetup.com. On the Meetup website, users can create or join a user group with the same interests, participate in interesting events and interact with the group. The event social network attracts a large number of users, and thus develops rapidly. However, the ever-increasing social network will generate a very large information flow, and it is difficult for users to quickly find their favorite activities, thus wasting a lot of t...

AI Technical Summary

Problems solved by technology

[0004] However, the above algorithms ignore the information of the event organizer, and in reality, the event organizer also plays a certain role in the user's decision-making process

At the same time, in the Bayesian Poisson decomposition model of Zhang et al., the event organizer is introduced as a random variable into the evaluation model, and other information is integrated to form the entire probability model. However, such a probability model has no historical records. For cold start events, there is no basis for recommendation, which affects the accuracy of event recommendation

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