Friend Recommendation System for Social Networks
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 5
Abstract
Abstract: The current social networking services suggests friends based on the respective individual’s network. This may not be the perfect way to recommend friends to respective user as friend suggestion should be more over focused on the real life friend selection style. In this paper, we present Friend Recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. In Friend Recommendation system we discovers life styles of users from user-centric 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 Collaborative Filtering with threshold 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, Friend Recommendation system returns a list of people with highest recommendation scores to the query user. We have implemented Friend Recommendation system on the Android-based smart phones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.
Authors and Affiliations
Anuja Shahane , Prof. Rucha Galgali
Keywords
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