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Case-Based Recommendation. Barry Smyth. Presented by Chul-Hwan Lee. 1. Introduction. 2. Retrieval & Recommendation. 3. Similarity & Diversity. 4. Conversation Technique. 5. Personalization Technique. Agenda. 1. Introduction. Origins of Case-Based Recommendation.

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Case-Based Recommendation

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Case-Based Recommendation

Barry Smyth

Presented by

Chul-Hwan Lee


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1.

Introduction

2.

Retrieval & Recommendation

3.

Similarity & Diversity

4.

Conversation Technique

5.

Personalization Technique

Agenda


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1.

Introduction

Origins of Case-Based Recommendation

Case-based Reasoning(CBR)

A model of reasoning that incorporates problem solving, understanding,

and learning, and integrates all of them with memory processes.

<- Soft Computing Methodology, Cognitive Science

[CBR prototype samples]

Cyrus, Mediator, Persuader, Chef, Julia, Casey, and Protos, CLAVIER

Case-based Recommendation:

A form of content-based recommendation that is especially well suited to many product recommendation Domains.

Case-base -> Well-structured Concepts Description

Content-base -> Less Structured Textual Item Description


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1.

Introduction

Origins of Case-Based Recommendation

Case

A contextualized piece of knowledge representing an

experience that teaches a lesson fundamental to achieving the goals of the system.

Case-based System

Operates through a process of remembering oneor a small set of concrete instances or cases and basing decisions on comparisonsbetween the new and old situations. – medical diagnosis, legal interpretation

Related Disciplines

Fuzzy Logic(FL), Neural Network(NN), Evolutionary Computing(EC), Probabilistic Reasoning(PR), Belief Networks, Chaos Theory, Parts of Learning Theory

Successful Tasks

Planning, Design, Diagnosis, Configuration, Classification, Prediction

Successful Domains

Manufacturing, Medical, Help-desks, Sales Support


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1.

Introduction

Major Components of a CBR system


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1.

Introduction

CBR Cycle

1. Retrieving similar previously experienced cases whose problem is judged to be similar

2. Reusing the cases by copying or integrating the solutions from the cases retrieved

3. Revising or adapting the solution(s) retrieved in an attempt to solve the new problem

4. Retaining the new solution once it has been confirmed or validated


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1.

Introduction

CBR Cycle


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1.

Introduction

Major Tasks of CBR


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1.

Introduction

Guidelines for the Use of CBR

1. Does the domain have an underlying model?

2. Are there exceptions and novel cases?

3. Do cases recur?

4. Is there significant benefit in adapting past solutions?

5. Are relevant previous cases obtainable?


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1.

Introduction

Advantages of Using CBR

1. Reducing the knowledge acquisition task.

2. Avoiding repeating mistakes made in the past.

3. Providing flexibility in knowledge modeling.

4. Reasoning in domains that have not been fully understood, defined, or modeled.

5. Making predictions of the probable success of a proffered solution.

6. Learning over time.

7. Reasoning in a domain with a small body of knowledge.

8. Reasoning with incomplete or imprecise data and concepts.

9. Avoiding repeating all the steps that need to be taken to arrive at a solution.

10. Providing a means of explanation.

11. Extending to many different purposes.

12. Extending to a broad range of domains.

13. Reflecting human reasoning.


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2.

Retrieval & Recommendation

Case Representation

Content-base Recommendation: unstructured or semi-structured manner, using keyword-based content analysis techniques

Case-based Recommendation: structured representations, using attribute-value representations techniques – fit into e-commerce domains

Nominal

Numeric


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2.

Retrieval & Recommendation

Similarity-based Retrieval

Similarity Assumption: most similar to the target problem, more sophisticated similarity metrics that are based on an explicit mapping of case features and the availability of specialised feature-level similarity knowledge.

t=target query, c=case, w=weight

Inverse relative difference (numerical)

  • For nominal data

  • Simple exact match metric (1 or 0)

  • Use domain knowledge

  • (similarity tables or similarity trees by domain knowledge expert)

Single-Shot Recommendation Problem: can be achieved by personalized recommendation through extended dialog with the user.


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3.

Similarity & Diversity


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3.

Similarity & Diversity

Bounded random selection & bounded greedy selection


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3.

Similarity & Diversity

Alternative Diversity-Preserving Approach

Similarity Layers

A set of cases, ranked by their similarity to the target query are partitioned into

similarity layers, such that all cases in a given layer have the same similarity

value to the query.

Order-based Retrival

constructs an ordering relation from the query provided

by the user and applies this relation to the case-base of products returning the

k items at the top of the ordering.

Compromise-driven Approach

the most similar cases to the users query are often not representative of compromises that the user may be prepared to accept.


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4.

Conversation Technique

Navigation by Asking

Employing Natural Language Processing(NLP), originated from conversational case-based reasoning systems.

An example conversational dialog between a user (the inquirer) and the Adaptive

Place Advisor recommender system (the advisor) in which the user is trying

to decide on a restaurant for dinner


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4.

Conversation Technique

Navigation by Proposing

Preference-based feedback, ratings-based feedback, critiquing-based feedback(dynamic compound critiques)

Entree recommends restaurants to users and solicits feedback in the form

of feature critiques. The screenshot a recommendation for Planet Hollywood along with

a variety of fixed critiques (less$$, nicer, cuisine etc.) over features such as the price,

ambiance and cuisine of the restaurant.


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4.

Conversation Technique

A screenshot of the digital camera recommender system evaluated in which solicits feedback in the form of fixed unit critiques and a set of dynamically

generated compound critiques. The former are indicated on either side of the

individual camera features, while the latter are presented beneath the main product

recommendation as a set of 3 alternatives.


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5.

Personalization Technique

User Modeling

User’s personal preference

User’s learned preference

Weak personalization to Strong personalization

Long term user preference information

This can be achieved by questionnaires, user ratings or usage traces, asking the user to weight a variety of areas of interest, normal online behavior patterns

Case-based Profiling

Profiling Feature Preferences


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Conclusions & References

Towards to Hybrid Recommendation System, Be~~tter System!

Explain the results of their reasoning

Justify their recommendations

In/With Product Space

Final Goal is to provide good system to the world!

That’s why we are here!

[1] Pal, S. & Shiu, S. Foundations of Soft Case-Based Reasoning, Wiley Series on Intelligent System, 2004.

[2] Smyth, B. & Cotter, P. A personalized Television Listing Service, Communications of the ACM, 2000, 107-111.

[3] Goker, M. & Thompson, C. Personalized Conversational Case-Based Recommendation, Journal of Artificial Intelligence Research, 2004, 1-36.

[4] Ricci, F., Cavada, D., Mirzadeh, N., & Venturini, A. Case-Based Travel Recommendations, 2005, Retrieved from http://ectrl.itc.it:8080/home/publications/2005/cbr-cabv3.pdf on Sep. 2005.


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Question or Comments?

http://www.sis.pitt.edu/~chlee

[email protected]


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