Analysis of Personalized Service in Social Network

2018 International Conference on Modeling, Simulation and Analysis (ICMSA 2018) ISBN: 978-1-60595-544-5

Analysis of Personalized Service in Social Network

Wei LI

, Cheng-cheng LI

and Pu LI

1_{Library, Shandong Normal University, Ji’nan 250014, China}

2_{School of Management Science and Engineering, Shandong Normal University, Ji’nan 250014,} China

*Corresponding author

Keywords: Social network, Personalization, Service.

Abstract. Social network has become a new world for people to communicate and share, and the personalized service adds more wonderful to social network. Through the way of questionnaires, the social network sites frequented by netizens, the preferences and the expectation of personalized services on social network sites are known. Through the analysis and research, the goal is to provide personalized services that can better meet the needs of users and to extend the life cycle of social network products, and this paper puts forward feasible suggestions on the future development of social network personalized services.

Introduction

France Nick Diani once said: "whenever a new civilization to the whole society, it will inspire new hopes and fears." Yes, indeed, when we were surprised to find that once the commodity you have ever seen appears in the following list of recommendations in the Amazon, do you have an impulse to turn off the cookie? However, we have to say that personalized service has actually brought people great convenience. In order to realize the perfect personalized service of social network, we need powerful server to store and analyse a variety of user information. Of course, it requires social network service providers to have strong hardware facilities, which is the basis for personalized service to attract customers.

Social network is no longer a simple platform for information sharing. Users can find their favorite application by using social networks, enterprises can rely on social network to implement marketing, both to achieve a win-win situation. Personalized service mode is divided into the following:

(1) According to the information that users fill in and pay attention to, which can analyze the user's preferences, and recommend accurate personalized service for users. For example, the application of libraries in social networks. On the one hand, libraries attract users, and on the other hand, they create new opportunities for interactive communities. In social network the small circle like library club was established. In the circle, the user can talk to each other, the library can analyses the user's preferences for the user push his favorite book information as well as a variety of related information, such as new book announcements, thematic training schedule and so on based on the content which users discussed and concerned about. At the same time, the club members of ordinary customers can use this convenient search library. This has brought great convenience for book lovers.

(2) DIY personalized service. Each user will have one of their own space in social networks, everyone wants to be out of the ordinary space of their own dress, if the user is a designer, so he has their own aesthetic values, of course want to design their own space, like the decoration of their houses, then you need space the appearance of personalized service to help. It can be said that the space dress is the best way to show your personality in social networks.

(4) Location service based on LBS. LBS-based location services are popular in many applications, using this service can not only increase the personalization of the user space, but also facilitate the search for close friends nearby. And some shopping or group buying sites provide users with nearby shopping or entertainment recommendation according to the user's location information.

Investigation and Analysis of Personalized Service Requirements

In order to understand the user demand for personalized service in social networks, we can understand the use situation of social network, the satisfaction of personalized services in the social network, as well as expectation of personalized service in a fashion of the questionnaire.

This survey has issued 150 questionnaires and 122 valid questionnaires were collected. There were 109 students account for 89.34% and 13 working people account for 10.66% in interviewees. In terms of gender, the percentage of the boys who were investigated is 22.95% and the girls is 77.05% of the total.

Analysis of Social Network Products and Personalized Services Frequently Used by Users

Figure 1. Social network products frequently used by users.

In the survey, social network products are frequently used by most Internet users are QQ space and Sina micro-blog. As shown in Fig 1, which shows personalized services and applications and user experience more close to people's individual needs of reality in QQ space and Sina micro-blog.

As can be seen from Fig 2, the most commonly used and favorite personalized service is basically the same, the largest proportion is the small station, concerned about the home page, radio class, while users hate the application is the shopping recommended class and game class.

The Influence of Gender and Occupation on User's Personalized Service

Figure 3. The influence of gender on the most commonly used personalized service for users.

It can be seen from Fig. 3, the personalized service is most often used by boys and girls are different, from which we can see girls than boys prefer space dress class applications and shopping class recommendation, but boys than girls prefer games class of recommended services.

Figure 4. The influence of occupation on the most commonly used personalized service for users.

Fig. 4 shows the different options for the most frequently used personalized service by students and working people, respectively. In comparison, students prefer the personalized service of space dressing, while those who already have jobs prefer shopping recommendation class more than students. This may be because the person who has worked has income and the needs of life, resulting in choosing more shopping class recommendation.

Conclusion

publisher. In the era of Web2.0, the user's participation is the key. The emergence of social networks is not only a platform for people to share information, but also a platform for people to show their own personality. Social networks move real interpersonal relationships into virtual networks, forming communities that simulate the real world. In this era of individuality pursuit, individuality is also incorporated into social network. Personalized service can not only bring convenience to yourself, show personalized self in their own space, also can bring precise advertising and marketing to businesses. More importantly, personalized service for the social network to increase the user

stickiness, retain the customer.[15] _{This allows social network products that can meet the needs of}

users to live longer in the social network of the ocean, in this era, the individual has become the key to survival.

However, to achieve personalization, we need to obtain a large number of user information, multi-level and multi angle analysis of user behavior, from which we can obtain the user's real preferences. And such a process first requires a powerful hardware device to store user information, followed by data mining capabilities and data analysis capabilities, because the user's information acquisition behavior is a complex behavior. But all this is not impossible, I believe in the future, we can see completely accurate personalized service, built entirely for ourselves.

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (No. 61170038, 61472231, 71701115), the National Social Science Foundation of China (No. 14BTQ049), the Shandong Natural Science Foundation (ZR2017MF058), and Special project for Internet development of social science planning special program of Shandong province (17CHLJ23).

References

[1] Li C, Zhu Z M, Ye J, Zhou J Y. Survey on research in personalization service [J]. Application Research of Computers, 2009, 26(11): 4001-4005+4009.

[2] Yu X J. Research on the Establishment of Personalized Teaching Mode Based on the Big Data’s Application [J]. Information Science, 2015, 33(11): 53-56.

[3] Gao M, Jin C Q, Qian W N, Wang X L, Zhou A Y. Real-Time and Personalized Recommendation on Microblogging Systems [J]. Chinese Journal of Computer, 2014, 37(04): 963-975.

[4] Chen K H, Han P P, Wu J. User Clustering Based Social Network Recommendation [J]. Chinese Journal of Computers, 2013, 36(02): 349-359.

[5] Yang K. Explore the Application of Social Network Analysis in Marketing [J]. Shopping Mall Modernization, 2017, (13): 254-255.

[6] Yang X C, Zhang X H. Application of Social Network Analysis in Marketing [J]. Contemporary Economy & Management, 2009, 31(06): 25-29.

[7] Lu J, Shambou R Q, Xu Y S. A Web Based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques [J]. Computational Intelligence, 2013, 1(29): 37-69.

[8] Wu B, Qi L, Feng X. Personalized Recommendation Algorithm Based on SVM [J]. International Conference on Communications, Circuits and Systems Proceedings, 2007 (1): 951-953.

[9] Neves A R, Carvalho A M, Ralha C G. Agent-Based Architecture for Context-Aware and Personalized Event Recommendation [J]. Expert Systems with Applications, 2014, 2(41): 563-573.

[11] Zeng Z M, Chen B B. Research on Personalized Service of Smart Library on the Context of Incorporation [J]. Library Forum, 2016, 36(02): 57-63.

[12] Zhu D. Personalized Archives Information Service Modes In the Social Media Environment [D]. Shanghai University, 2015.

[13] Xu P. The Study Of Brand Positioning Models Based on Consumer's Cognition [D]. Nankai University, 2009.

[14] Guo R L. Research and Implementation of Location-based Service System Based on Mobile Terminal [D]. Harbin Engineering University, 2013.