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 Friendbook, 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 Smartphone, 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. 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.
People typically made friends with others who live or work close to themselves, such as neighbors or colleagues. We call friends made through this traditional fashion as G-friends, which stands for geographical location-based friends because they are influenced by the geographical distances between each other. With the rapid advances in social networks, services such as Facebook, Twitter and Google+ have provided us revolutionary ways of making friends. According to Facebook statistics, a user has an average of 130 friends, perhaps larger than any other time in history. One challenge with existing social networking services is how to recommend a good friend to a user. Most of them rely 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. Unfortunately, this approach may not be the most appropriate based on recent sociology findings.
Disadvantages of Existing:
-It does not meet the user needs.
-It is not appropriate method to recommend friends.
Our proposed solution is also motivated by the recent advances in Smartphone, which have become more and more popular in people’s lives. These Smartphone’s (e.g., iPhone or Android-based Smartphone) are equipped with a rich set of embedded sensors, such as GPS, accelerometer, microphone, gyroscope, and camera. Thus, a Smartphone is no longer simply a communication device, but also a powerful and environmental reality sensing platform from which we can extract rich context and content-aware information. From this perspective, Smartphone’s serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. In spite of the powerful sensing capabilities of Smartphone’s, there are still multiple challenges for extracting users’ life styles and recommending potential friends based on their similarities. First, how to automatically and accurately discover life styles from noisy and heterogeneous sensor data? Second, how to measure the similarity of users in terms of life styles? Third, who should be recommended to the user among all the friend candidates? To address these challenges, in this paper, we present Friendbook, a semantic-based friend recommendation system based on sensor-rich Smartphone’s.
Advantages of Proposed System:
Friendbook is the first friend recommendation system exploiting a user’s life style information. It use the probabilistic topic model to extract life style information of users.
Latent Dirichlet Allocation algorithm
Suppose You Have The Following Set Of Sentences:
I like to eat broccoli and bananas.
I ate a banana and spinach smoothie for breakfast.
Chinchillas and kittens are cute.
My sister adopted a kitten yesterday.
Look at this cute hamster munching on a piece of broccoli. What is latent Dirichlet allocation? It’s a way of automatically discovering topics that these sentences contain. For example, given these sentences and asked For 2 topics, LDA might produce something like
Sentences 1 and 2: 100% Topic A
Sentences 3 and 4: 100% Topic B
Sentence 5: 60% Topic A, 40% Topic B
Topic A: 30% broccoli, 15% bananas, 10% breakfast, 10% munching … (at which point, you could interpret topic A to be about food)
Topic B: 20% chinchillas, 20% kittens, 20% cute, 15% hamster … (at which point, you could interpret topic B to be about cute animals)