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Recommendations based on Social Relationships in Mobile Services
Author(s) -
Qi Jiayin,
Zhu Chao,
Yang Yanwu
Publication year - 2014
Publication title -
systems research and behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 1092-7026
DOI - 10.1002/sres.2279
Subject(s) - scarcity , collaborative filtering , computer science , product (mathematics) , similarity (geometry) , recommender system , set (abstract data type) , social media , the internet , mobile telephony , world wide web , data science , telecommunications , artificial intelligence , mobile radio , economics , geometry , mathematics , programming language , image (mathematics) , microeconomics
The scarcity problem in user–product matrix has become severe, which is affecting the recommendation system efficiency in mobile services; it is also related to social networks and Internet of Things, where huge amount of data and complex relationships exist. This paper proposes a novel recommendation approach based on social relationships between users to handle the scarcity problem and facilitate recommendations in mobile services. We define a model of social relationships based on a set of call detail record factors of telecom users and design a vacancy‐filling method to reduce the scarcity of the user–product matrix. An integrated similarity measure is provided to improve the filtering rules of neighbours of the target user. Then, we build a new recommendation system based on social relationships, with mobile services in the telecom industry as the application. Furthermore, we conductexperiments with the real‐world data of voice calls, and experimental results show that the filling method proposed can effectively reduce the scarcity of the user–product matrix and our social relationships approach outperforms the collaborative filtering in terms of the call, precision and mean absolute error indicators. Copyright © 2014 John Wiley & Sons, Ltd.