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1Building Recommendations by means of Classification Based on Fuzzy Association

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50 pages
1Building Recommendations by means of Classification Based on Fuzzy Association Joel Pinho Lucas Supervisor: María N. Moreno García (MIDA Research Group) Collaboration: Anne Laurent, Maguelonne Teisseire (LIRMM, TaToo) PhD Program in Computer Science Department of Computer Science and Automatics University of Salamanca, Spain

  • employ recommender

  • based

  • fuzzy association ?

  • future works ?

  • find products

  • cba-fuzzy algorithm ?

  • scenario ?


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1
Building Recommendations by means of
Building Recommendations by means of
Classification Based on Fuzzy Association
Classification Based on Fuzzy Association
Joel Pinho Lucas
Supervisor: María N. Moreno García (MIDA Research Group)
Collaboration: Anne Laurent, Maguelonne Teisseire (LIRMM, TaToo)
PhD Program in Computer Science
PhD Program in Computer Science
Department of Computer Science and Automatics
Department of Computer Science and Automatics
University of Salamanca, Spain
2
Outline
Outline
Scenario
Scenario
Recommender Systems
Recommender Systems
Association Rules
Association Rules
Classification Based on Association
Classification Based on Association
Case Studies
Case Studies
Results
Results
Hybrid Recommendation Method
Hybrid Recommendation Method
Fuzzy Logic and Fuzzy Association
Fuzzy Logic and Fuzzy Association
CBA-Fuzzy Algorithm
CBA-Fuzzy Algorithm
Conclusions and Future Works
Conclusions and Future Works
References
References
3
Scenario
Scenario
It has been estimated that the amount of information in
the world doubles every 20 months (Breese, et. al.)
It is technologically feasible to store and publish huge
volumes of data with, more and more, lower costs
Nowadays it is harder to process and extract valuable
information from databases
4
Scenario
Scenario
E-commerce Systems:
The
information explosion
denoted nowadays is reflected by
loads of products available for sale
Difficulty in choosing and purchasing products
They need to personalize the presentation of their products
They employ recommender systems to help consumers in order
to find products to purchase (Thabtah et. al.)
5
Recommender Systems
Recommender Systems
Examples: MovieLens, Video Recommender, Amazon,
etc.
They can be divided into two main classes:
Content-based methods:
compare text documents
to user profiles
Collaborative filtering methods:
are based on
opinions of other users (
ratings
)
Memory based (User-based)
Model based (Item-based)