Case-Based Recommendation

1. Case-Based Recommendation Barry Smyth Presented by Chul-Hwan Lee

2. 1. Introduction 2. Retrieval & Recommendation 3. Similarity & Diversity 4. Conversation Technique 5. Personalization Technique Agenda

3. 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

4. 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

5. 1. Introduction Major Components of a CBR system

6. 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

7. 1. Introduction CBR Cycle

8. 1. Introduction Major Tasks of CBR

9. 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?

10. 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.

11. 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

12. 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.

13. 3. Similarity & Diversity

14. 3. Similarity & Diversity Bounded random selection & bounded greedy selection

15. 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.

16. 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

17. 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.

18. 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.

19. 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

20. 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.

21. Question or Comments? http://www.sis.pitt.edu/~chlee chlee56@pitt.edu