/
Chapter 20: Chapter 20:

Chapter 20: - PowerPoint Presentation

alida-meadow
alida-meadow . @alida-meadow
Follow
377 views
Uploaded On 2015-11-21

Chapter 20: - PPT Presentation

Social Service Selection ServiceOriented Computing Semantics Processes Agents Munindar P Singh and Michael N Huhns Wiley 2005 Chapter 20 2 ServiceOriented Computing Semantics Processes Agents ID: 200854

agents service processes chapter service agents chapter processes michael oriented computing semantics huhns munindar singh services referrals social recommender

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "Chapter 20:" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

Chapter 20:Social Service Selection

Service-Oriented Computing:

Semantics, Processes, Agents

– Munindar P. Singh and Michael N. Huhns, Wiley, 2005Slide2

Chapter 202

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Highlights of this Chapter

Reputation Mechanisms

Recommender Techniques

Referrals

Social Mechanism for Trust

IdentitySlide3

Chapter 203

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Recommending Products vs. Services

Products (by a product vendor): often,

The recommender is the provider

Votes are known to the recommender

Votes are received prior to usage (buying)

Repetition is less likely (buy the same book?)

Services (by a service registry)

The recommender is not the provider

Votes are not necessarily known to recommender

Votes are given after usage

Repetition may occur, invisibly to registrySlide4

Chapter 204

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Reputation

Computationally treated as centralized

The agency (e.g., eBay) is the

central authority

that

Authenticates users

Records, aggregates, and reveals ratings

Provides the

central conceptual

schema for

How to capture ratings (e.g., numbers and text)

How to aggregate them

How to decay them over timeSlide5

Chapter 205

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Service Communities

Each principal

Provides services to

each others

Provides recommendations to others

Exploits services provided by others

Has a reputation

The agents assist their users and other agents in

Evaluating the services and referrals provided by others

Maintaining models of acquaintances

Deciding whom to contact for a serviceSlide6

Chapter 206

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Social Networks and Referral Chains

Referral chains in networks provide:

Way to identify a good provider

Way to judge the quality of a provider

Reason for a member to respond in a trustworthy manner

As the chains get longer

The trustworthiness of a recommendation decreases

The effort to find providers increases

Therefore, shorter chains are

better

Technical challenge: how can we find such?Slide7

Chapter 207

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Distributed Treatment of Referrals

Receive request

Model asker

Respond

Ask

Follow referrals

Use

Rate; updateSlide8

Chapter 208

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Agent Model for Referrals

Model each agent via its

Interest (services sought)

Expertise (services provided)

Models of its acquaintances representing their

Expertise (ability to provide good services)

Sociability (ability to provide accurate referrals)Slide9

Chapter 209

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Reputation Buildup and Collapse

A participant who begins to misbehave is detectedSlide10

Chapter 2010

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Small World Phenomenon

Milgram (1967): two individuals chosen at random in the U.S.A. are linked by a chain of 6 or fewer first-name acquaintances (empirical observation)

Six degrees of separation

Erdös numbers

Diameter of the connected Web: 20Slide11

Chapter 2011

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Small-World Network

Generated by perturbing a regular ring

A highly structured (clustered) network with just a few random edges

Random edges correspond to shortcuts

Yields high clustering and short paths

Direct relationships between agents who primarily belong to distinct subcommunities

Shortcuts: weak tiesSlide12

Chapter 2012

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Quality Relates Inversely to ClusteringSlide13

Chapter 2013

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Weak Ties versus Clustering

Conventional approaches recommend based on preferences of similar users

But, it is better to ask dissimilar people who bring a novel perspective

Define a form of

controlled scatteringSlide14

Chapter 2014

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Link Analysis

Web link: recommendation by page author

An external criterion for estimating the value of a page to others

Typically, web engines crawl the web, build giant indexes, and analyze links

Referral: dynamic, targeted recommendation by an agent

Similar mathematical concepts to above applySlide15

Chapter 2015

Service-Oriented Computing: Semantics, Processes, Agents

- Munindar Singh and Michael Huhns

Chapter 20 Summary

Selection should be

empirical:

based on data

Centralized reputation mechanisms gather data but impose many restrictions

Research Challenges

Social

networks can avoid

the above limitations

Referrals help maintain distributed social networks

Social structure can evolve collaboratively

Services can be rated and selected and rated again, and so on …