Research On Influencing Factors Of Channel’s Friend Like Recommendation Mechanism On Users’ Use Intention

Short video from 2011 to now,there have been more than ten years of development period,experienced the emergence and explosion,to now the market is basically mature.Channels since the launch of Tencent in early 2020 to now,a short period of rapid growth,has initially occupied a seat,and forming a situation of tripartite confrontation tiktok and Kwai..Short video commonly used algorithm to recommend content,although the realization of accurate push,but the mechanism also exists "algorithm discrimination","algorithm black box" and other issues.Based on the specificity of We Chat’s social attributes,channels optimization recommendation mechanism,based on the original traditional algorithmic recommendations,a new social distribution mechanism of recommendations-"friends",to achieve better information distribution effect.This paper focuses on the special "friends" recommendation function of channels,and explores the factors influencing users’ use of the "friends" function,as well as the optimization effect of social recommendation on traditional algorithmic recommendation,the results of which have certain reference value for users,content producers and platform side.The research results have certain reference value for users,content producers and platforms.This study takes "friends",a new content distribution function of channels,as an example to explore the factors influencing users’ participation intention and behavior in social recommendation.Based on relevant domestic and international studies,and taking into account the characteristics of channels,the study establishes a model of factors influencing We Chat users’ willingness to use the recommendation function of "Friends" by adding two variables,namely privacy concern and perceived trust,on the basis of an integrated technology acceptance and usage model and distributing questionnaires online.A total of 607 questionnaires were distributed online,and 530 valid questionnaires were collected,with an effective rate of 87.3%.The study proved that the five variables of effort expectation,performance expectation,social influence,perceived trust,and enabling conditions all positively influenced the users’ willingness to use channels,while privacy concern negatively influenced the willingness to use.Based on the questionnaire analysis combined with the interviews,this study concluded that the user usage rate of channels is high,the group role is significant,and social combined with algorithm can build a healthy recommendation mechanism.However,channels still faces the problems of low user dwell time and strong privacy protection behavior.It is suggested that on the basis of attaching importance to social recommendation,the benefit distribution mechanism should be further improved,while paying attention to user privacy and setting up a recommendation mechanism for grouping likes.By combining social recommendation with algorithmic recommendation,the information dissemination effect is optimized.