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ISDA 2011 Cordoba

ISDA 2011 Cordoba






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    ISDA 2011 Cordoba ISDA 2011 Cordoba Presentation Transcript

    • 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!
    • 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
    • SOMAR (1)
      • SOMAR can be divided into: i) generation of the recommender and ii) application of the recommender.
    • 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
    • Computing the Social Graph
      • The graph computation in the mobile goes on in three steps:
      • mutual friend computation
      • user clustering
      • affinity degree calculation
    • 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.
    • 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
    • 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.
    • 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
      • 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
      • 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
    • 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
    • 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