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Pricing Information Goods in an Agent-based Information Filtering System

Pricing Information Goods in an Agent-based Information Filtering System. Laura Maria Andreescu. Christos Tryfonopoulos MPII Saarbrücken. David Midgley INSEAD Fontainebleau. Outline:. Motivation Background ABIS Publisher selection and ranking Experiments Conclusions. results.

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Pricing Information Goods in an Agent-based Information Filtering System

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  1. Pricing Information Goods in an Agent-based Information Filtering System Laura Maria Andreescu Christos Tryfonopoulos MPII Saarbrücken David Midgley INSEAD Fontainebleau

  2. Outline: • Motivation • Background • ABIS • Publisher selection and ranking • Experiments • Conclusions

  3. results Information Retrieval Scenario: simple query: “financial crisis” Agent

  4. notifications Information Filtering (IF) Scenario: continuous query: “financial crisis” Agent

  5. Applications of IF: • News dissemination • Sharing educational material (e.g., Edutella) • Information alert for digital libraries • Information alert for electronic marketplaces • Stock market updates • …

  6. Information Filtering System Architecture Overview: Publisher1 Publisher2 meta-data middleware request for meta-data meta-data Publisher3 continuous query Subscriber1 notify meta-data = e.g. # docs available, publication rate

  7. Exact IF vs. Approximate IF: • Exact IF • Subscribers are interested in notifications for all publications • Approximate IF • Subscribers are NOT interested in all matching publications • Trade recall for scalability

  8. ABIS: Multi-agent Arhitecture AgentNetwork DirectoryService Publication Service Subscription Service

  9. financial crisis financial finance: A1,A3,A4 crisis crisis: A1,A2,A5 ABIS: Example crisis: A1,A2,A5 financial crisis: A1,A2,A3,A4, A5 finance: A1,A3,A4 finance: A1,A3,A4 Agent 5 Agent 6 Agent 2 Agent 4 crisis: A1,A2,A5 Agent 1 Agent 3

  10. ABIS: Example Agent 5 Agent 6 finance … crisis … Agent 2 no notification finance … crisis … Agent 4 Agent 1 Agent 3

  11. Low Average High 60% 30% 10% ABIS: • information has a price • 3 classes of agents • choosing best top-k publishers that would monitor his query

  12. Publisher selection: • Given a query q: Which agents are most likely to publish documents matching q in the future? • Subscriber uses the directory service (collecting per-term statistics of each query term) to compute publisher scores Publisher Score Information Quality Price = = • Publisher score used for ranking • Scores are periodically recomputed, queries repositioned

  13. Information Quality: Information Quality Resource Selection Agent Behaviour Prediction Resource Selection • identifies authorities • based on IR techniques: term/document frequencies, collection sizes... • how likely is a agent to publish documents of interest in the future • based on time series analysis on IR metrics Agent Behaviour Prediction

  14. Experimental Evaluation: • Setup: • 100 agents containing • 10 categories: Music, Finance, Arts, Sports... • each agent: initial collection of 300 documents • 15% random documents • 10% not categorized • 75% documents from a single category • 10 agents specializing in each category • 30 continuous queries • comparison with MAPS (Minerva Approximate Publish/Subscribe System)

  15. Prices: Random Prices Prices Correlated Strictly with Quality Spearman footrule metric adaptation Prices Partly Correlated with Quality 1 Prices Partly Correlated with Quality 2 quality price

  16. Publishing behaviour: Recall Consistent Publishing Change in Publishing quality price

  17. Conclusions: • Contributions • Define an agent based arhitecture for approximate information filtering • Proposal publisher ranking technique • resource selection • predicted behaviour • cost of information • Future Work • Money Flow Publisher-Subscriber (in progress) • Automatic Adjustment of Prices

  18. Thank you!

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