1. Toward Personalized
Peer-to-Peer Top-k Processing
Xiao BAI
Marin BERTIER
Rachid GUERRAOUI
Anne-Marie KERMARREC
31 March, 2009

2. Outline
 Background and Motivation
 Personalized Peer­to­Peer Top­k Processing
 Evaluation
 Conclusion
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3. Collaborative tagging and Top-k
 Collaborative tagging systems
 U(sers) * I(tems) * T(ags)
 Tagged(u, i, t): User u annotates item i with tag t
 Top­k Processing
Item i Scoretj(i)
 Inverted list per tag
 Query q = {t1, ..., tn}
 Score(i) = f (Scoret1(i), ..., Scoretn(i))
 k items with highest scores as results
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4. Motivations
 Why collaborative tagging?
 Flexibility of folksonomy
 Searching photos, videos, ...
 Why personalization?
 Variety of user preferences
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5. How to personalize?
 Network­aware search
 Interest­based personal network
 Network(u) = {vi | link(u,vi )}
 link(u,v) iff Strengthlink(u,v) =
|{i | Tagged(u, i, t)&Tagged(v,i, t)}| > threshold
 Personalized score
 Scoretj(i, u) = | Network(u) ∩{v | Tagged(v, i, tj)}|
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6. Why peer-to-peer?
 Exact
 Inverted list per (user, tag)
 Space intensive
 Global Upper­Bound
 Inverted list per tag Item i UB(i, tj) users(i)
 Time consuming: Scoretj(i) at query time
 User Clustering
 Inverted list per (cluster, tag)
 Trade­off between Exact and Global Upper­Bound
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7. Scalability & Efficiency
calls for
Decentralization
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8. Peer-to-Peer Solution
 Goals
 Top­k results as centralized network­aware search
 Time consumption as Exact
 Limited storage per user
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9. Personal network discovery
 Two­layer gossip protocol
 Top layer: Personal network
 Measure the similarities between users
 Bottom layer: Random Peer Sampling
 Keep the overlay connected
 Feed top­layer with new related peers
 Gossip framework
 Peer selection
 Data exchange
 Data processing
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10. Inverted lists & Query processing
 Inverted list per (user, tag)
 Inverted lists built and stored by concerned user
 Q = (u, t1, ... , tn)
 Query processed locally with NRA
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11. NRA (No Random Access)
 Score(i) = ∑ tj∈Q Scoretj(i)
 Inverted lists are scanned sequentially in parallel
 Information for each seen item:
 Score lower­bound
 Score upper­bound
 Seen items are sorted on score lower­bound
 Termination condition: lower­bound(k­th seen item) ≥
max{ upper­bound(i) | i∈{Seen items}/{top­k}}
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12. Personal network optimization
 At most n users per personal network
 Derived from all users meeting the threshold
 Strategies
 Random
 Biased Random: plink(u, v)∝ Strengthlink(u, v)
 Nearest: highest Strengthlink(u, v)
 Nearest With Enhanced Link Strength
 Strengthlink(u, v)=| { (i, tj) | Tagged(u, i, tj) & Tagged(v, i, tj) } |
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13. Evaluation
 Implementation
 PeerSim
 Data
 Real trace from del.icio.us
 10,000 users
 101,144 items
 31,899 tags
 9,536,635 tagging actions
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14. Evaluation metrics
 Convergence speed
1 ∣Current Network {u }∣
 Speed = ∑
∣U∣ u∈ U ∣Network {u } of Centralized Setting∣
 Top­k quality: Recall
Number of Retrieved Relevant Items
 Rk = Total Number of Relevant Items
 Storage space per user
 Length of inverted lists
 Length of neigbhors' profiles
 Processing time
 Number of sequential accesses
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15. Convergence speed
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16. Recall
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17. Storage space
 Inverted lists  Profiles
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18. Processing time
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19. Comparison of optimization
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20. Scalability
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21. Conclusion
 Decentralization is the right way to provide scalable
personalized top­k processing
 Future work
 Churn
 Privacy
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22. Thank you!
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