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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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 2
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 3
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]
March 11 - 15, 2007 ACM SAC, Seoul, Korea 4
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 5
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]
March 11 - 15, 2007 ACM SAC, Seoul, Korea 6
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 7
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 8
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
Seria lizableObjects
Over SOAP
Seria lizableObjects
Remote C lient Adapters
Spring Proxies/Adapters
R e mote M oduleAO P H e lpe rs
ExceptionManager
LoggingService
Seria lizableObjects
IC-Serv ice
SIC
S C
ore
March 11 - 15, 2007 ACM SAC, Seoul, Korea 9
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 10
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 11
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 12
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’
March 11 - 15, 2007 ACM SAC, Seoul, Korea 13
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 14
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 15
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 16
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 17
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
March 11 - 15, 2007 ACM SAC, Seoul, Korea 18
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…
March 11 - 15, 2007 ACM SAC, Seoul, Korea 19
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)