Group Clustering And Recommendation Based On Nash Equilibrium

With the rapid development of Internet technology,the scale of data in the Internet is increasing day by day.In order to obtain useful information in the data and provide targeted services for users,the research of recommendation system becomes very important.In recent years,there are more and more cases that the concept of Nash equilibrium is applied to promote the development of services in the computer field.Therefore,how to integrate Nash equilibrium into the recommendation system to improve the effect of group recommendation has become a new research hotspot.At present,most group division methods often ignore the social relationship of users and the selfish behavior of users,and ignore the influence of Extreme users in groups.In addition,the group recommendation system ignores the interaction of group members,which is contrary to the reality and even affects the quality of recommendation.Although some researches have considered this problem,they simply rely on the user right to reflect the interaction,which not only does not produce real interaction,but also causes the fairness problem.In view of the above problems,this thesis conducts research on group division and recommendation,with the main work as follows:From the perspective of group clustering,a group clustering method based on Nash equilibrium is proposed for the positive impact of users' social relations and selfish behavior combined with Nash equilibrium theory.This method first considers the social relations among members according to the cost of group members and models the individual selection of group members according to the social relations.Then,the game rules are formulated through the sequence of selfish preferences to simulate the selection of group members.Finally,the Nash equilibrium points are obtained to achieve the effect of stable group clustering and eliminate the extreme users.The simulation results show that this method has higher accuracy and satisfaction than other methods.From the perspective of group recommendation,a group recommendation method based on Nash equilibrium is proposed to solve the problems of user interaction and fairness that are ignored by most of the existing group recommendation methods.First,the scores of the group members on the unseen items are captured by completing the matrix.Then we find the Nash equilibrium solution to simulate the selection of members to generate interaction.The payment function is set up to consider the acceptance degree of other members when selecting a member.At this time,themember will make the best choice than the group.Finally,the preference aggregation method is used to get the group preference.The simulation results show that the proposed group recommendation method is effective.Based on the above research methods,this thesis designs a group recommendation prototype system based on Nash equilibrium.In addition,it introduces the components of the system,the operation process and its structure in detail,verifies the feasibility of the method proposed in this thesis,and shows the recommendation effect of the group division method based on the Nash equilibrium and the group recommendation method based on the Nash equilibrium under the real and complex network conditions.The prototype system realizes the method proposed in this thesis in the actual application scenario,and shows its effectiveness and feasibility through the effect.