Friend-book A Semantic-Based Friend Recommendation System for Social Networks
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 Friend-book, 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, Friend-book 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, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, 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.Friend-book A Semantic-Based Friend Recommendation System for Social Networks
EXISTING SYSTEM:
Most of the friend suggestions mechanism relies on pre-existing user relationships to pick friend candidates. For example, Facebook relies on a social link analysis among those who already share common friends and recommends symmetrical users as potential friends. The rules to group people together include:
- Habits or life style
- Attitudes
- Tastes
- Moral standards
- Economic level; and
- People they already know.
Apparently, rule #3 and rule #6 are the mainstream factors considered by existing recommendation systems.
PROPOSED SYSTEM:
- 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, Friendbook 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.
- 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.
- 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.
- We integrate a linear feedback mechanism that exploits the user’s feedback to improve recommendation accuracy.
SCREENSHOTS:
