LINKZONE SOCIAL NETWORKING RECOMMENDER SYSTEM

Main Article Content

DR. KOTRAPPASIRBI
DR. KSHAMA V. KULHALLI
MR. ABHIJIT J. PATANKAR

Abstract

Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Linkzone, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Linkzone discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph. Upon receiving a request, Linkzone returns a list of people with highest recommendation scores to the query user. Finally, Linkzone integrates a feedback mechanism to further improve the recommendation accuracy.

Article Details

How to Cite
DR. KOTRAPPASIRBI, DR. KSHAMA V. KULHALLI, & MR. ABHIJIT J. PATANKAR. (2021). LINKZONE SOCIAL NETWORKING RECOMMENDER SYSTEM. JournalNX - A Multidisciplinary Peer Reviewed Journal, 105–108. Retrieved from https://repo.journalnx.com/index.php/nx/article/view/2040