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Motivating Cooperation in Peer-to-Peer Communities Helen Bretzke 1, Julita Vassileva 2 Computer Science Department 1 University of Toronto 2 University.

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Presentation on theme: "Motivating Cooperation in Peer-to-Peer Communities Helen Bretzke 1, Julita Vassileva 2 Computer Science Department 1 University of Toronto 2 University."— Presentation transcript:

1 Motivating Cooperation in Peer-to-Peer Communities Helen Bretzke 1, Julita Vassileva 2 Computer Science Department 1 University of Toronto 2 University of Saskatchewan Canada

2 Outline Peer-to-peer communities –free-riding and participation User modeling – social type of user (w.r.t. cooperativeness) – user interests – user relationships Motivating Participation – rewards in terms of QoS – creating community awareness – promoting user reflection

3 What are Peer to Peer (P2P) systems? Networked applications (“servents”) that act as both servers (producers) and clients (consumers) of shared resources. Networked applications (“servents”) that act as both servers (producers) and clients (consumers) of shared resources. Shared resources can be files, bandwidth, computation cycles or human time and effort. Shared resources can be files, bandwidth, computation cycles or human time and effort.

4 COMTELLA A P2P (Gnutella based) system for file sharing and service –users share academic papers, code snippets, help non-centralized digital library for a research group / class motivation and community-building Christopher Cox NSERC Summer 2002 project Helen Bretzke CRA-W and NSERC Summer’ 2002 project Lingling Sun undergraduate project Yamini Upadrashta Graduate student

5 Resentful peers call them “leeches”... What is a free-rider? The press calls them “cyberparasites”

6 A proper definition: A free-rider is a user who consumes far more resources than s/he offers. “almost 70% of Gnutella users share no files, and nearly 50% of all responses are returned by the top 1% of sharing hosts” (Adar & Huberman, 2000)

7 What’s the real problem? Many call free-riding the “tragedy of the digital commons” Others claim that this a poor analogy.

8 In any online community where there are costs associated with sharing, free- riding can bring the system to its knees. …e.g. I-Help Beyond P2P

9 I-Help deployment results 2 years, users, all undergrad CS classes at the UofS, also in the UK, France and Colombia Lessons learned: Usage / participation varies greatly Should be perceived as adding value After reaching a “critical mass” becomes self-feeding Encouraging students to participate is crucial Greer J., McCalla G., Vassileva J., Deters R., Bull S., Kettel L. (2001) Lessons Learned in Deploying a Multi-Agent Learning Support System, Proceedings AIED'2001, IOS Press: Amsterdam

10 Why do they do it? In the centralized, client- server systems of yore, users grew accustomed to being served. But in the decentralized world of P2P, they must also learn to contribute! Users are lagging in a paradigm shift.

11 How can we convert piglets into peers? cultivate user understanding of her new role in this new paradigm. create a perception of the P2P network as a community of volunteers. create a perception of the P2P network as a community of volunteers. generate and promote a strong sense of this community. generate and promote a strong sense of this community.

12 Levels of participation Bring new files, give help Provide disk space / processor time Dispatch requests Stay on-line Use and quit

13 socially motivated Why do people offer their time and resources? Different people have different motivations: materialistic Some are altruists  Some would help their friends and hope to make new friends through helping  Some seek glory  Some seek high marks  or money… How to motivate participation? altruistic

14 Know your user! User Type: Altruist? Socialist? Materialist? User Interests: What does she search / need? User Relationships and Community: Who shares interest with the user? Potential “friends” and “bozos”. Modelling

15 Modelling user type Monitor user’s actions regarding file sharing, relative time spent on-line, acts of interrupting service, total balance of user’s giving / taking Update a number in [-1, 1] representing user’s cooperativenessUpdate a number Motivational actions in the interface triggered by passing certain thresholds

16 Define a taxonomy of subject categories (e.g. ACM subject index) Keep track of the categories of queries (  user interests) Update user level of interest in each sub- category using reinforcement learningreinforcement learning Keep track of resources or services offered by the user in each interest category Modelling user interests

17 Modelling user relationships Monitor who offers services in the user’s areas of interest, whose services the user chooses, the quality of the service, and who uses services offered by the user Represent each user relationship: For each area of interest –Strength – how often, how successful service (reinforcement learning used, similar to user interests) –Balance – reciprocity of services used/ givenreciprocity of services used/ given Adapt P2P topology – form a neighborhood for search using the best relationships (“friends”) in the area of search Propagate farther queries of “friends”

18 Motivating participation Reinforcing / rewarding relationships Friends are treated differently –Transfers not interrupted –Queries processed with priority –Queries are propagated farther Clusters of friends sharing an area of interest Queries sent to friends in the area –Higher chance of having relevant files –Faster responses –Better quality of files Better Quality of Service!

19 Motivating participation Building a community Harness the UM to gather information about the user’s interests, friends and cooperativeness. Provide feedback to stimulate reflection and awareness in the user.

20 Social awareness In cities, the sidewalks provide the right kinds and numbers of interactions from which neighborhoods emerge. In isolation, selfishness is logical. To gain perspective, users require feedback about their social environment.

21 A matter of scale An astronomical metaphor Provides visual feedback Resolves scale Attractive & interesting

22 Views of the community connectivity (hop-graph of currently reachable peers) shared interest clusters overall ranking of peers (closest friends are those of greatest utility)

23 Shared interests

24 Ranking of peers Brightness ~ reciprocity Size ~ strength of relationship Position ~ overall rank Visual semantics

25 Prompting reflection Highlight cause and effect relationship between user actions and QoS. Give real-time feedback unobtrusive peripheral animations ‘whispered’ messages (soft text) If user is curious, she can read the text

26 Evaluation results Difficult to evaluate the effects of many features at once –separate experiments –first stage: to evaluate the impact of modelling user relationships on the QoS –two methods for evaluation: through simulation through experiment with human subjects –first results presented in W5 paper

27 “take-home” messages Creating community awareness Modelling social aspects of user behaviour – has the potential to increase user understanding of her new role in the network and to stimulate more cooperative behavior. – as an engine for generating personalized community views – for book-keeping needed to reward participation – to adapt the environment and assure better QoS

28 the end… Not

29 Computing user type The measure of user cooperativeness at time t C(w t, t) = i * C(w t-1, t-1) + (1 - i) * w t, w  [-1,0)  (0,1] represent the weight of evidence, where w 0 is an altruistic act. overallBalance = (1/n)*  Y (B XY ) userType = (cooperativeness + overallBalance) /2 If userType is in [-1, -0.5) then user is selfish, if it is in [-0.5)  ( 0.5] then user is reciprocal, and if it is in (0.5, 1] then user is altruistic.

30 Computing user interests Reinforcement learning / exponential smoothing The user’s strength of interest S in an area a is calculated based on how frequently and how recently the user has searched in this area. S a (e t, t) = i * S a (e t-1, t-1) + (1 - i) * e t where e t  [0, 1] is calculated as e t = 1/ d, d = 1 + level_distance between the level of the sub-area of the query and the level of the area a in the ontology hierarchy. Currently, the ontology hierarchy has only 2 levels, so e t = 0.5

31 Computing the balance of a relationship B XY = (N X  Y  N Y  X ) / (N X  Y  N Y  X ) B XY  [-1, 1] N X  Y - number of times X took from Y N Y  X - number of times Y took from X


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