Design And Implementation Of Event Recommendation System Based On Group Relationship And Preference Aggregation

With the development and popularization of social network services,people pay more and more attention to social events in their daily life.People inevitably contact each other to participate in group events,and it is more and more convenient to get together to form a lasting or accidental group.Accurate event participation prediction could guide the event organization and the community development more effective.Therefore,the importance of recommending appropriate event to groups is gradually increasing.The main task of our study is to use group member information and historical data to provide accurate events recommendations for the group,so as to improve the recommendation system performance and user experience;The main work of our study could be summarized as follows:(1)In order to scientifically aggregate different member preferences and form the group event participation decision,this paper proposes a group event recommendation model based on preference aggregation(ASIGR),which dynamically calculates the social influence weight of users in the group by combining the user concern relationship and user activity factors in the group through network representation learning algorithm and stack denoising selfcoder.We form the group event participation decision according to the social influence weight of users.We conducted a large number of experiments on real-world dataset,the results show that our algorithm has a better recommendation performance.(2)In order to model the social relationship between groups and explore the impact of group communication on event recommendation,this paper proposes an event recommendation model based on group relationship extraction(Gro-MAGRM),which is used to extract the explicit and implicit relationship vectors between different groups from the bipartite graph of group event.After the joint optimization we could obtain the group social relationship vector.This model could obtain the group representation vector with the group social relationship vectors and group co-occurrence vector,group descriptive vector.We conducted a large number of experiments on two real-world datasets,the results show that our algorithm has a better recommendation performance.(3)In order to solve the problem of preference transfer in group events,this paper proposes a group event recommendation model based on preference transfer(So-GLS),which obtains the user’s long-term and shortterm representation vector through graph representation learning,and introduces the attention mechanism network learning to obtain weight of vector for the fusion of the user representation vector to represent the transfer of user activity participation preference.We conducted a large number of experiments on two real-world datasets,the results show that our algorithm has a better recommendation performance.(4)Based on the above three group event recommendation models,this paper designs and implements the group event recommendation system-group interest,which can effectively recommend appropriate events for group with different sizes and activity,and improve the recommendation accuracy and bring users a better use experience...