march 11 - 15, 2007acm sac, seoul, korea1 ic-service: a service-oriented approach to the development...

March 11 - 15, 2007 ACM SAC, Seoul, Korea 1

IC-Service: A Service-Oriented Approach to the Development of Recommendation

SystemsAliaksandr Birukou, Enrico Blanzieri, Vincenzo

D'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena


Introduction Recommendation systems Service-Oriented Computing Implicit Culture System for Implicit Culture Support

(SICS) SICS Architecture

Main modules Configuration

Applications Web service discovery

Conclusions References


Recommendation systems Prune large information spaces in

searching for items of interest Examples

movies (MovieLens), music (JUKE-BOX), books (Amazon), hotels (TripAdvisor) …

Meta-recommendation systems Work with data from multiple

(heterogeneous) information sources MetaLens [Schafer et al., 2002]


Service-oriented computing

Service Registry

Service Client

Service Provider

Publ

ish B

ind

Find

Service-oriented application

Web service description

Web servic

e

Requirements for a recommendation service: Use in various application domains Ability to store heterogeneous client data Adaptability to the needs of a particular client Ability to process data according to the domain

specific rules


Implicit Culture (IC): motivation and goals Communities of human/artificial agents

have knowledge specific to their activities, i.e., community culture

The knowledge is often implicit and highly personalized

Encourage a newcomer to behave according to a community culture

Transfer knowledge implicitly (without special efforts for its analysis and description)

http://www.dit.unitn.it/~implicit

[Blanzieri et al., 2001]

http://www.dit.unitn.it/~implicit


IC definitions Action – something that can be done Agent (actor) – somebody or something performing an

action Object – something that passively participate in the

action Situation – a state of the world faced by the agent.

Includes a set of objects and a set of possible actions Culture – a usual behavior of the group of agents Group G – group of agents which behaviour is observed Group G' – group of agents who require recommendations Implicit Culture relation – situations in which agents of the

group G behave similarly to agents of the group G' System for Implicit Culture Support (SICS) – a system

which tries to establish IC relation

Observeagents’ actions

Extractactions performed

in different situations

Suggestactions in

a given situation


System for Implicit Culture Support (SICS)

D B o f o bs e rv a tio ns

Inductiv e M o dule

C ultura l A c tio n F ind er

S ceneP ro d ucer

C o mpo s e r

th e o ry

a g e n ts , o b je c ts , a c t io n s , s c e n e s

s c e n e s

d o ma in th e o ry

o b s e rv a t io n s

o b s e rv a t io n s

s c e n e s

O bs e rv e r

a g e n ts , o b je c ts , a c t io n s , s c e n e s

Stores information

about actions

Produce a theory about common user

behaviorProduce recommendation

about action


SICS Architecture SICS Core

SICS layerinfers theory rules and recommends actions

Configuration and storage layermanages theory

SICS Remote Module

defines protocols for information exchange with the client

SICS Remote Clientprovides a simple interface for remote clients Core

AOPHelpers

SIC

S C

ore

Co

nfi

gu

rati

on

an

d

Sto

rag

e L

aye

r

Configuration Module

Rule Storage Module

Storage Module

SIC

S L

aye

rComposer Adapters

Composer

Inductive Module

Application

SIC

S R

em

ote

Cli

en

tS

ICS

Re

mo

te M

od

ule

R e mote M oduleAO P H e lpe rs

ExceptionManager

LoggingService SICS Adapters

Spring Proxies/Adapters

AxisEJB

Seria lizableObjects

Over RMI


Over SOAP


Remote C lient Adapters

Spring Proxies/Adapters

R e mote M oduleAO P H e lpe rs

ExceptionManager

LoggingService


IC-Serv ice

SIC

S C

ore


Storage Module

Theory rules if consequent (predicates)

then antecedent (predicates)

Predicates: Conditions on

observations (action- predicates)

Conditions on time (temporal-predicates)

Database Storage

Storage Adapters

XML Storage

XQuery/XPathUtilities

JDomUtilities

Java/XMLTransformers

H ibernate Library

Database XML Files

Observations Agents (1…N), Actions (1), Objects (0…N), Attributes (0…N) Scenes (1…N)

no agents no timestamps

Storage Adapters

XML Storage

XQuery/XPathUtilities

JDomUtilities

Java/XMLTransformers

XML Files


Inductive Module

Analyses observations and generates theory rules for an actor or a group of actors

“Apriori” algorithm for mining association rules [Agrawal & Srikant, 1994] A transaction is a sequence of executed actions A1,…,AN

(can be obtained from observations using timestamps) An association rule is an implication of the form A1 A2

where A1, A2 are actions, A1 A2 The rule holds with confidence c if c% of transactions

that contain A1 also contain A2 The rule A1 A2 has support s in the transaction set s%

of transactions contain A1 A2 Generate association rules that have support and

confidence greater than predefined minimum support and minimum confidence.

Inductive Module Apriori Implementation

Apriori R ulesGenerator

Apriori AlgorithmOther algorithms


Composer Module

Composer U tilities

Composer Implementation

CAF UtilitiesSimilarity U tilities

Cultural Action Finder (CAF) Matches actions executed by agents from group

G’ with antecedents of the theory rules Matching algorithms

Returns consequences of the theory rules (cultural actions)

Scene producer Finds a set of agents that have performed actions

similar to a cultural action for the agent X Selects a set of agents similar to an agent X and a

set of scenes S in which they have performed the actions

Select and propose to X a scene from S


Instance ConfigurationInstance Configuration

Inductive Module Configuration

Inductive ModuleConstants

Composer Configuration

ComposerConstants

Configuration OfSimilarity Functions

XML DefinitionLoader Sim ple Class

W rapper

XML file

Configuration of similarity functions: Rules for calculating similarity among observations Similarity weights for elements (names and values)

exceptions, instants and default Case sensitive or not Regular expressions

Inductive Module constants

Composer constants: Similarity threshold Number of nearest

neighbors Return all scenes or only

the best Max number of

observations Names of groups G and

G’


Applications Prototypes:

Recommending Web links [Birukou et al., 2005]

Recommending scientific publications Quality-based Indexing of Web

Information (QUIEW) http://quiew.itc.it/ Supporting Polymerase Chain Reaction

(PCR) experiments [Mullis et al., 1986] [Sarini et al., 2004]

Software patterns selection Web service discovery

http://quiew.itc.it/




Web Service (WS) discovery Meeting functionality required by a user

with specifications of existing web services Problems: incomplete specifications, broken links,

unfair providers…

Choosing a service with good quality characteristics Problems: often QoS data are not available, some

of them are context-dependent…

Implicit Culture approach Analyze which web services have been previously

used for similar problems by clients with similar interests

Use up-to-date information to improve service discovery and QoS-driven selection


A system for WS discoveryIC-ServiceRemote Client (Proxy)Developer

Request(query)

Request(query)

Application

Invoke(operation, input){}

Feedback

Recommend(operations)

{OR}

Application IC-Service Web ServiceRemote Client (Proxy)

Invoke(operation, input)

Report(invoke, operation, input)

Invoke(operation, input)

Respond(output)

{OR}Raise(exception)

Report(respond, operation, output)

Report(exception, operation, input)

Respond(output)

Raise(exception) {OR}

Feedback

Search proces

s

Monitoring

process


WS discovery in terms of IC Observations

Actors Applications (application name, user name, location) Users (user name, location)

Objects Operations (operation name, web service name) Inputs/Outputs (parameter name, parameter value) Requests (goals, operations, inputs/outputs)

Actions Invoke (timestamp, operation, input) Get response (timestamp, operation, output, response time) Raise exception (timestamp, operation, exception type, input) Provide feedback (timestamp, QoS parameters) Submit request (timestamp, request)

Rules if submit request (request) then invoke (operation-

X(service-Y), request).

Similarity measures: Vector Space Model (VSM)

Term Frequency- Inverse Document Frequency (TF-IDF) metric WordNet-based semantic similarity measure


A system for WS discovery: experimental results 20 web services (http://www.xMethods.com) divided

into 5 categories [Kokash et al., 2007] 4 clients submit 100 requests

VSM

WordNet

http://www.xmethods.com/


Conclusions Ubiquity

The IC-service can be accessed from any workplace Reusability

A unique solution for various distributed communities

Integration The knowledge transfer between communities is

facilitated Scalability

100000 observations of 100 users for one instance Composition of several IC-Services is possible

Portability XML storage

Customization Ability of runtime configuring of theory rules…


References [Schafer et al., 2002] J. B. Schafer, J. A. Konstan, and J. Riedl. Meta-

recommendation systems: user-controlled integration of diverse recommendations. In Proc. of the Int. Conference on Information and Knowledge Management, pages 43-51. ACM Press, 2002.

[Blanzieri et al., 2001] E. Blanzieri, P. Giorgini, P. Massa, and S. Recla. Implicit culture for multi-agent interaction support. In CooplS: Proc. of the 9th Int. Conference on Cooperative Information Systems, volume 2172 of LNCS, pages 27-39. Springer, 2001.

[Birukov et al., 2005] A. Birukov, E. Blanzieri, and P. Giorgini. Implicit: An agent-based recommendation system for web search. In AAMAS: Proc. of the 4th Int. Joint Conference on Autonomous Agents and Multiagent Systems, pages 618-624. ACM Press, 2005.

[Mullis et al., 1986] K. B. Mullis, F. A. Faloona, S. Scharf, R. K. Saiki, G. Horn, H. A. Erlich. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. In Cold Spring Harbor Symposia on Quantitative Biology, volume 51, pages 263-273, 1986.

[Sarini et al., 2004] M. Sarini, E. Blanzieri, P. Giorgini, C. Moser. From actions to suggestions: supporting the work of biologists through laboratory notebooks. In COOP: Proc. of 6th Int. Conference on the Design of Cooperative Systems, pages 131-146. IOS Press, 2004.

[Agrawal & Srikant, 1994] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB: Proc. of the 20th Int. Conference on Very Large Data Bases, pages 487-499. Morgan Kaufmann, 1994.

[Kokash et al., 2007] N. Kokash, A. Birukou, V. D'Andrea: Web service discovery based on past user experience. In: International Conference on Business Information Systems (BIS), to appear, Springer (2007)

march 11 - 15, 2007acm sac, seoul, korea1 ic-service: a service-oriented approach to the development...

Documents

actionacm sac

recommendation service

remote clientsacm sac

ic relationacm sac

group gsystem

alessio modenaacm sac

serviceoriented approach

koreaimplicit culture