pricing information goods in an agent-based information filtering system

Christos Tryfonopoulos MPII Saarbrücken

David Midgley INSEAD Fontainebleau

Pricing Information Goods in an Agent-based Information Filtering

System

Laura Maria Andreescu


Pricing Information Goods in an Agent-Based Information Filtering System

Oberseminar 31.10.2008Oberseminar | 31 October 2008 | MPII Saarbrücken

Outline:

Motivation Background ABIS Publisher selection and ranking Experiments Conclusions




Information Retrieval Scenario:

simple query:“financial crisis”

resultsAgent




Information Filtering (IF) Scenario:

continuous query:“financial crisis”

notificationsAgent




Applications of IF:

News dissemination Sharing educational material (e.g., Edutella) Information alert for digital libraries Information alert for electronic marketplaces Stock market updates …




Information Filtering System Architecture Overview:

middleware

Publisher3

Publisher2

Publisher1

Subscriber1

meta-data

request for meta-datameta-data

continuous query

meta-data = e.g. # docs available, publication rate

notify




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




ABIS: Multi-agent Arhitecture

Agent Network

Directory Service

Subscription ServicePublication Service




ABIS: Example

Agent 6

Agent 4

Agent 1

Agent 2

Agent 5

finance: Afinance: A11,A,A33,A,A44

crisis: Acrisis: A11,A,A22,A,A55

financial crisisfinancial crisis

crisis

financial



Agent 3



financial crisis:financial crisis:AA11,A,A22,A,A33,A,A44, A, A55




ABIS: Example

Agent 6

Agent 4

Agent 1

Agent 2

Agent 5

Agent 3

financefinance……crisiscrisis……

financefinance……crisiscrisis……

nonotification




ABIS:

information has a price 3 classes of agents

choosing best top-k publishers that would monitor his query

Low Average High

60% 30% 10%




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 selection:

Publisher Score Information Quality Price==

Publisher score used for ranking

Scores are periodically recomputed, queries repositioned




Information Quality:

Information Quality Resource Selection Agent Behaviour Prediction

Resource Selection

Agent Behaviour Prediction

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




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)




Prices:

Random Prices

Prices Correlated Strictly with Quality

Prices Partly Correlated with Quality 1

pricequality

Spearmanfootrule metric

adaptation

Prices Partly Correlated with Quality 2




Publishing behaviour:

Change in Publishing

Consistent Publishing

pricequality

Recall




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




Thank you!

pricing information goods in an agent-based information filtering system

Documents

mpii saarbrckenabis

mpii saarbrckenoutline

mpii saarbrckenapplications

mpii saarbrckengiven

mpii saarbrckenexact

a4financial crisis

simple query

query q