Predicting User Geographical Service Rating in Social Network Using Point of Interest Recommendation
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2017, Vol 4, Issue 9
Abstract
Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. Online Social Networks (OSNs), which attract thousands of million people to use everyday, also greatly extend OSN users‟ social circles by friend recommendations. OSN users‟ existing social relationship can be characterized as 1-hop trust relationship, and further establish a multi-hop trust chain during the recommendation process. As the same as what people usually experience in the daily life, the social relationship in cyberspaces are potentially formed by OSN users‟ shared attributes, e.g., colleagues, family members, or classmates, which indicates the attribute-based recommendation process would lead to more fine grained social relationships between strangers. The import social networking sites are Facebook, Twitter, LinkedIn, WhatsApp, Google plus. Social networks are constituted Because of its user group‟s common interest in some social emerging issues. The popular social Networking sites are Facebook, Twitter, LinkedIn, whatsapp, Google plus etc. which are actually online social networking sites. And mainly the large amount of online users and their special interests possess great challenges to support recommendation of friends on social networks for each of the users. However, with the popularity of public cloud services, the main concern of confidentiality is recognized as the problem even for personal individual users The proposed Friend Recommendation framework shows good accuracy for social graphs used as model dataset.
Authors and Affiliations
D. Anil Kumar
Keywords
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