Dynamic clustering process to calculate affinity degree of users

as basis of a social network recommender

A. Zanda, S. Eibe and E. Menasalvas

Universidad Politécnica de Madrid

SoWeTrend@WISE1228-30 November 2012, Paphos, Cyprus

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Outline

• Introduction and motivation • Preliminaries on SN data• Social graph• Social graph update• Experiments• Conclusion and Future work

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Introduction • Social networking is a reality.

year

% of world

population

[source: marketer]

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Introduction

• Lot of data being shared in SN– very difficult to manage information;– users loose interesting pieces of news.

And in mobile devices?

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Introduction

• In previous work we have presented a recommender based on SN data (SOMAR) [1].– recommends activities based on the user social network;– for mobile devices.

• Get interesting information only• Not overloading of information for users

[1] SOMAR: a social mobile activity recommender. ESWA 2012.

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Introduction

• SOMAR recommendations are based on a social graph– represents the user connections;

• GOAL: update the social graph in a mobile device dynamically.

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Outline

• Introduction and motivation • Preliminaries on SN data• Social graph• Social graph update• Experiments• Conclusion and Future work

SoWeTrend@WISE12

SN Data

• Actor and relations Vs actors and attributes– features as relationships with others

– Social Graph

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Outline

• Introduction and motivation • Setting the problem• Preliminaries on SN data• Social graph• Social graph update• Experiments• Conclusion and Future work

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Social graph

Hypothesis: “users tend to have social interactions only with a small group of their social network friends”

•The social graph represents the relationships of a user with his friends showing how frequently the user interacts with them.

•key characteristics:•(i) the nodes of the graph can be friends or groups of friends;

•(ii) the graph is based on mutual friendship and the quantity of relationships among users.

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Social graph computation

• Input: all SN data accessible to a user (ROOT)

• Step 1 - Mutual friend computation: finds the number of mutual friendships between ROOT users.

• Step 2 - User clustering: groups the users according the number of mutual friends.

• Step 3 - Affinity degree calculation: gets a measure of affinity between ROOT and all the groups found in Step2.

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Social graph - Step 1

• compute the number of mutual friends of each friend with all the other Root’s friends.

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Social graph - Step 2

• Using hierarchical clustering:

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Social graph - Step 3

• The weight of the edge connecting the Root to a node i is given by:

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Outline

• Introduction and motivation • Preliminaries on SN data• Social graph• Social graph update• Experiments• Conclusion and Future work

SoWeTrend@WISE12

Social graph update

• Hypothesis: the interaction of users change over their lifetime.

• GOAL: update the social graph.• The change in the social graph involves:

– user interests;– degree friendship among users.

• How to update? Update mining models!– Integrate the autonomous mining configurator [2]

[2] Adapting batch learning algorithms execution in ubiquitous devices. MDM 2010.

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Social graph update

• How to integrate the autonomous configurator?• Calculate the behavior model (EE-Model) of the

DM algorithm

• Method:• Selecting the DM algorithm: K-medoids (clustering);• Obtain a dataset of historical executions of algorithm;• Apply machine learning techniques to learn a model

of behavior from the historical executions.

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Outline

• Introduction and motivation • Preliminaries on SN data• Solution: social graph• Social graph update• Experiments• Conclusion and Future work

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Experiments

• Historical dataset analysis (1)

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Experiments

• Historical dataset analysis (2)

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Experiments

• A model of behavior for CPU cycles

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Experiments

• A model of behavior for error

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Outline

• Introduction and motivation • Preliminaries on SN data• Solution: social graph• Social graph update• Experiments• Conclusion and Future work

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Conclusions

A social graph for suggesting items to users

The integration of a behavior model to update the social graph

Future work: collect real data to test the performance of the social graph.

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