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ISDA 2011 Cordoba
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ISDA 2011 Cordoba


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  • 1. INTRO
    • Social networking is generating an incredible amount of information that is sometimes difficult for users to process
    • Social networking is now supported by mobile devices. The analysis of on-the-move interactions in social networks is now gaining further attention from researchers
    • Several links, activities, and recommendations are proposed by networked friends every hour, which together are nearly impossible to manage
    • Therefore there is a need to filter and make accessible such information to users
    • This is the motivation behind developing a mobile recommender that exploits social network information
    • We propose a Social Mobile Activity Recommender that integrates Facebook social network mobile data and sensor data to propose activities (concerts, ongoings, semiars, etc) to the mobile users
    • The recommendations are completely calculated in the mobile device with an embedded data mining component
    • Endowing mobiles with the capability to generate data mining models it may be possible to create a new smart world!
  • 3. SOcial Mobile Activity Recommender (SOMAR)
    • SOMAR provides recommendations on the basis of the users social relationships
    • Hypothesis: Social networks users tend to have social interactions only with a small group of their social network friends
    • Information sources:
      • mobile data in terms of call history or contacts
      • sensor data in terms of sensor location
      • Facebook data
  • 4. SOMAR (1)
    • SOMAR can be divided into: i) generation of the recommender and ii) application of the recommender.
  • 5. Generation of the recommender
    • The recommendations are based on the user social realtionships, for this reason we present the Social Graph.
    • The aim the Social Graph is:
      • to represent the relationships of a user with the users friends or subgroups of friends
      • show how frequently the user interacts with them
    • The characteristics of the Social graph are:
      • the nodes represent friends or groups of friends
      • the graph is based on mutual friendship and the quantity of relationships among users
  • 6. Computing the Social Graph
    • The graph computation in the mobile goes on in three steps:
    • mutual friend computation
    • user clustering
    • affinity degree calculation
  • 7. Computing the Social Graph (1)
    • Mutual Friend Computation
    • Information required:
      • the list of friends of the user
      • the list of friends of users’s friendes.
    • For each pair of friends, the number of mutual friends is calculated
    • Output: a matrix M (n*n) of mutual friendship, where n is the numer of the user’s friends.
  • 8. Computing the Social Graph (2)
    • User Clustering
    • The aim is to group the users, the measure of affinity is given by the number of mutual friends
    • For this aim we propose to use the hierarchical clustering taking as input the matrix of mutual friendship M
    • The output is a number of clusters which represent the connections among friends, independently from the Root user being analyzed
    • In fact, a greater number of mutual friends shared by two users results in a higher probability that they will be in the same clusters
  • 9. Computing the Social Graph (3)
    • Affinity Degree Calculation
    • In this step we determine the degree of the affinity between the Root and the clustered friends
    • The edges connecting nodes are labeled with a value based on the number of relationships among users, which in turn establishes the degree of affinity between those nodes
    • The relationships are defined by: comments, likes, messages etc.
    • The final output is a weighed graph representing the social relations of a user.
  • 10. Application of the Recommender
    • Activity recommendation requires the following online tasks:
    • Activity recommendation requires the following online tasks:
    • Data Integration:
      • We will provide a module that integrates and prepares data from sensors, the phone and Facebook
    • Activity recommendation requires the following online tasks:
    • Data Integration:
      • We will provide a module that integrates and prepares data from sensors, the phone and Facebook
    • Activity Recognition:
      • Activities are easily detectable because they are published by the event Facebook application
    • Activity Matching
      • The social graph is used to select activities among those detected by the Activity Recognizer
      • The weights between the nodes in the social graph represents how socially close is the user proposing the activities to the target (Root node)
      • This weight is then compared with a threshold based on the social graphs structure and degree, and it can be modified by the user
      • Finally, activities are flaged considering, for example, location and current user activities
  • 15. CASE STUDY
    • In this paper we only focus in the analysis of the feasibility and the performance of generating the social graph in the mobile, in particular:
      • Complexity of step 1
      • Clustering behaviour depending on the number of friends
      • Affinity degree calculation of the basis of the number of clusters
    • We carried out the experiments in a system with a 2.16 GHz Core 2 processor and 2.5GB 667 MHz DDR2 SDRAM memory
  • 16. CASE STUDY
    • Data about the target user include the friend list, public wall and all information shared by friends in the friend list
    • In particular, the user in this case study has:
      • A friend list that contains 149 friends
      • Data comprise 1379 posts generated over two years
      • Data in post include links, photos, likes,comments and tags that involve two or more users
  • 17. RESULTS (performance analysis)
    • STEP 1 has complexity O(n^m), where n is the number of the Root’s friends and m is the maximum number of friends connected to each of the Root’s friend.
    • STEP 2: CPU time grows up to 13 seconds when the number of friends is maximum
  • 18. RESULTS (performance analysis) (2)
    • STEP 3 has a linear complexity of O(k), where k is the number of identified clusters.
    • SOMAR analyses and compute information locally in the device
    • Local computation provides several advantages:
      • It's guaranteeing the privacy of sensitive information
      • It's providing personalized and context aware recommendations
    • The main drawback is relates to resource comsuption and performance
    • The main cost is related to computation of the social graph
    • It depends on the number of friends of the user
    • Nevertheless, for the user (real) used in the experiments having 149 friends has been shown the feasibility of the method
    • For the rest of the steps of the social graph generation, according to the experiments, they are feasible for the maximum number of friends allowed on Facebook