Recommandé
Recommandé
Contenu connexe
Similaire à WISE 2012
Similaire à WISE 2012 (20)
WISE 2012
- 1. 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@WISE12 28-30 November 2012, Paphos, Cyprus
- 2. Outline • Introduction and motivation • Preliminaries on SN data • Social graph • Social graph update • Experiments • Conclusion and Future work SoWeTrend@WISE12
- 3. Introduction • Social networking is a reality. [source: marketer] % of world population year SoWeTrend@WISE12
- 4. Introduction • Lot of data being shared in SN – very difficult to manage information; – users loose interesting pieces of news. And in mobile devices? SoWeTrend@WISE12
- 5. 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. SoWeTrend@WISE12
- 6. Introduction • SOMAR recommendations are based on a social graph – represents the user connections; • GOAL: update the social graph in a mobile device dynamically. SoWeTrend@WISE12
- 7. Outline • Introduction and motivation • Preliminaries on SN data • Social graph • Social graph update • Experiments • Conclusion and Future work SoWeTrend@WISE12
- 8. SN Data • Actor and relations Vs actors and attributes – features as relationships with others – Social Graph SoWeTrend@WISE12
- 9. Outline • Introduction and motivation • Setting the problem • Preliminaries on SN data • Social graph • Social graph update • Experiments • Conclusion and Future work SoWeTrend@WISE12
- 10. 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. SoWeTrend@WISE12
- 11. 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. SoWeTrend@WISE12
- 12. Social graph - Step 1 • compute the number of mutual friends of each friend with all the other Root’s friends. SoWeTrend@WISE12
- 13. Social graph - Step 2 • Using hierarchical clustering: SoWeTrend@WISE12
- 14. Social graph - Step 3 • The weight of the edge connecting the Root to a node i is given by: SoWeTrend@WISE12
- 15. Outline • Introduction and motivation • Preliminaries on SN data • Social graph • Social graph update • Experiments • Conclusion and Future work SoWeTrend@WISE12
- 16. 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 201 SoWeTrend@WISE12
- 17. 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. SoWeTrend@WISE12
- 18. Outline • Introduction and motivation • Preliminaries on SN data • Solution: social graph • Social graph update • Experiments • Conclusion and Future work SoWeTrend@WISE12
- 19. Experiments • Historical dataset analysis (1) SoWeTrend@WISE12
- 20. Experiments • Historical dataset analysis (2) SoWeTrend@WISE12
- 21. Experiments • A model of behavior for CPU cycles SoWeTrend@WISE12
- 22. Experiments • A model of behavior for error SoWeTrend@WISE12
- 23. Outline • Introduction and motivation • Preliminaries on SN data • Solution: social graph • Social graph update • Experiments • Conclusion and Future work SoWeTrend@WISE12
- 24. 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. Online tool: www.eventa.cc SoWeTrend@WISE12
Notes de l'éditeur
- Good afternoon, my name is andrea zanda and I present a work on how to generate a social graph for a recommender system and how to update it in case of change. In case of change of the user interaction with the social network.
- The structure of the presentation is the follow. We first introduce the problem and give the motivation of what we are doing and before presenting the solution, we discuss about social networks data and their implications for data mining tasks We then present our particular implementation of the social graph and a mechanism to update it autonomously In order to validate the social graph update we present some experiments and to end we summarize the work and present future directions.
- I want to start with numbers, social networking is a reality, 1.4 billion (17%) people use social networks and the trends we have taken from [Marketer] says that they will be more than 2 billions around 25 % of the world population. the part of the graphic in dark blue represents the percentage of mobile social network users, which are also increasing (in 2010 their usage more than doubled and in the future, always accoing to marketer source thay will be around 15% of the worldwide population)
- the social networks extend our abilities in keeling relations with people. without any technology we are able to interact up to 150 friends. So SNs extend this capability... the problem ia that a lot of data is being shared in SN and it is very difficult to manage such information. It is easy then to loose interesting information in this scenario. But what about mobile devices? It is also more complicated, because we have smaller screens, the interfaces are not as easy as the in a personal computer etcetera...
- For this reason we have presented a recommender systems which is based on social network data. The items of the recommendation are activities (conferences/ events in general / friends meetings) and it is created to be used in mobile devices. The main advantage is that the users are not overloaded of information and they can get only the interesting one.
- The core of the recommender is the social graph, the representation of the user connections in the graphic we can see our proposal for social graph that we will describe better in the next slides now we can state the goal of this paper which is to update the social graph in a mobile device dynamically, so without human intervention.
- Let’s talk a bit about Social network data, the main difference is that instead of having actors and attributes we have actors and relations. in practice in a social network setting we do not only have information about age, address, country.. name but we also know how he is interacting with other users... -righ normal case right how this interaction is made explicit
- our hypotesis is that users do not interact with hteir friends in the same way, but they have interaction with a small group of their friends. and the objective of the social graph is to represent relations with their friends and how often they interact! The social graph we have presented is characterized by 2 aspects: the nodes can be also group of friends... that we are going to explore in detail....
- How to generate a social graph? The information need is all the information we can access from a user account, friendslists, public posts, tags ects
- after we get the mutual friend matrix we apply the clustering in order to get groups of users. On the left we can see the root user in the middle and his friends, after the clustering we obtain the result of the right, some of the will be in the same group.
- now that we have the groups, we have to calculate the affinity degree which defines how the root the a certain node are close. We define this with the following formula:
- The change is normal in life and we expect it is also normal in social relations defined by interactions in social networks we have found two keys for discovering change in the social graph... LIST Once we have have described how to generate the social graph there is a problem in the case we want to compute dynamically the social graph. The step 2 has a data mining algorithm, so we should recompute it for the social graph regeneration. For this we use a mechanism we have published in 2010 which autonomously configure the data mining algorithm.
- we carried out the process we have described for the clustering algorithm needed to update the social graph and we analysze some of the results we have reported in the paper
- for of al we analyze the historical dataset of executions... it has been obtained by changing input and by monitoring the output in terms of resource consumption of the algorithm and accuracy of the resutls. so in the graph we can notice the
- in the second graphic we have the number of clusters in comparison to a quality measure of the output: the error.
- to the historical dataset we have analyzed we have applied the DM predictive techniques so here we analyze the models...