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IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems. Aliaksandr Birukou, Enrico Blanzieri, Vincenzo D'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena. Introduction. Recommendation systems Service-Oriented Computing Implicit Culture

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Ic service a service oriented approach to the development of recommendation systems l.jpg

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

Aliaksandr Birukou, Enrico Blanzieri, VincenzoD'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena

ACM SAC, Seoul, Korea


Introduction l.jpg
Introduction of Recommendation Systems

  • Recommendation systems

  • Service-Oriented Computing

  • Implicit Culture

  • System for Implicit Culture Support (SICS)

  • SICS Architecture

    • Main modules

    • Configuration

  • Applications

    • Web service discovery

  • Conclusions

  • References

ACM SAC, Seoul, Korea


Recommendation systems l.jpg
Recommendation systems of 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]

ACM SAC, Seoul, Korea


Service oriented computing l.jpg

Service of Recommendation Systems

Registry

Publish

Bind

Find

Service

Client

Service

Provider

Service-oriented computing

Web service

description

  • 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

Service-oriented

application

Web service

ACM SAC, Seoul, Korea


Implicit culture ic motivation and goals l.jpg
Implicit Culture (IC): of Recommendation Systemsmotivation 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]

ACM SAC, Seoul, Korea


Ic definitions l.jpg

Extract of Recommendation Systems

actions performed

in different situations

Suggest

actions in

a given situation

Observe

agents’ actions

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 Gbehave similarly to agents of the group G'

  • System for Implicit Culture Support (SICS) – a system which tries to establish IC relation

ACM SAC, Seoul, Korea


System for implicit culture support sics l.jpg
System for Implicit Culture Support (SICS) of Recommendation Systems

Produce a theory about common user behavior

Produce recommendation about action

Stores information about actions

ACM SAC, Seoul, Korea


Sics architecture l.jpg
SICS Architecture of Recommendation Systems

  • SICS Core

    • SICS layer

      infers theory rules and recommends actions

    • Configuration and storage layer

      manages theory

  • SICS Remote Module

    defines protocols for information exchange with the client

  • SICS Remote Client

    provides a simple interface for remote clients

ACM SAC, Seoul, Korea


Storage module l.jpg
Storage Module of Recommendation Systems

  • Observations

    • Agents (1…N),

    • Actions (1),

    • Objects (0…N),

    • Attributes (0…N)

    • Scenes (1…N)

      • no agents

      • no timestamps

  • Theory rules

    • if consequent (predicates) then antecedent (predicates)

    • Predicates:

      • Conditions on observations (action- predicates)

      • Conditions on time (temporal-predicates)

ACM SAC, Seoul, Korea


Inductive module l.jpg
Inductive Module of Recommendation Systems

  • 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 implicationof the form A1 A2 where A1, A2 are actions, A1 A2

    • The rule holds with confidencec 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.

ACM SAC, Seoul, Korea


Composer module l.jpg
Composer Module of Recommendation Systems

  • 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

ACM SAC, Seoul, Korea


Instance configuration l.jpg
Instance Configuration of Recommendation Systems

  • 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’

  • 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

ACM SAC, Seoul, Korea


Applications l.jpg
Applications of Recommendation Systems

  • 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

ACM SAC, Seoul, Korea


Web service ws discovery l.jpg
Web Service (WS) discovery of Recommendation Systems

  • 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

ACM SAC, Seoul, Korea


A system for ws discovery l.jpg
A system for WS discovery of Recommendation Systems

Search

process

Monitoring

process

ACM SAC, Seoul, Korea


Ws discovery in terms of ic l.jpg
WS discovery in terms of IC of Recommendation Systems

  • 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

ACM SAC, Seoul, Korea


A system for ws discovery experimental results l.jpg

VSM of Recommendation Systems

WordNet

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

ACM SAC, Seoul, Korea


Conclusions l.jpg
Conclusions of Recommendation Systems

  • 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…

ACM SAC, Seoul, Korea


References l.jpg
References of Recommendation Systems

  • [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)

ACM SAC, Seoul, Korea


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