Research of Multichannel User Data to Identify the Degree of Similarity

A. Asmaryan, A. Levanov, Irina Borovik
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Abstract

This chapter introduces the method to assess similarity based on Facebook Graph API and user movements. All movements of users are collected and analyzed. The chapter presents an additional method for analyzing user-generated images on Instagram Graph API. The chapter presents a two-step multiparameter algorithm that generates recommendations based on user social activity and movements. A flexible mechanism for the calculations of time that one spends on a variety of social activities to more accurately identify the relationships between users is presented. To reduce the load on the application, the algorithms of data analysis and transfer optimization are proposed. The ultimate result of the study is to build a platform based on the “client-server” model and includes a mobile app on the iOS platform and server, which would be set up on the “LAMP” platform (L - Linux operating system, A - Apache web server, M - MySQL database, P - PHP programming language). The given result can be used and applied in various spheres of our lives to identify different relationships between people.
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多通道用户数据相似度识别研究
本章介绍了基于Facebook Graph API和用户移动来评估相似性的方法。用户的所有动作都会被收集和分析。本章介绍了在Instagram Graph API上分析用户生成图像的另一种方法。本章介绍了一种基于用户社交活动和运动生成推荐的两步多参数算法。提出了一种灵活的机制,用于计算人们在各种社会活动上花费的时间,以更准确地识别用户之间的关系。为了减少应用程序的负载,提出了数据分析和传输优化算法。研究的最终结果是建立一个基于“客户端-服务器”模型的平台,包括一个在iOS平台和服务器上的移动应用程序,该平台将建立在“LAMP”平台上(L - Linux操作系统,a - Apache web服务器,M - MySQL数据库,P - PHP编程语言)。给定的结果可以用于并应用于我们生活的各个领域,以确定人与人之间的不同关系。
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