1. Designing a framework For Recommender system Based on Interactive Evolutionary Computation Date : Mar 20 Sat, 2011 Project Number : 2011-0007561

2. CONTENTS 1. Introduction 2. Research Overview 3. Preliminary Experiment 4. Conclusion

3. Introduction

4. In daily life... I’m wondering which one is BEST for me? It is hard to make decision. Is there any one who help me? Don’t worry! I can help you. Recommender System said... 4

5. Recommender System ‣ recognize user’s preference ‣ recommend items that are interesting and useful to user. This System’s Purpose is to 5 iTunes

6. Research Overview

7. Research Goal ‣ o‣ overcome existing methods’ limitation ‣ guarantee quality of recommendation. The main objective is to Content-Based Filtering CollaborativeFiltering Existing Method Over specialization Sparse Problem & Content-Based Filtering IEC** Proposed Method ** = Interactive Evolutionary Computation 7

8. Research Plan There are two separate parts of plan. New Recommendation Method Music Recommender System 1st Period (2011.03.01 ~ 2012.02.28) Consider Improving Points Music Recommender System [Improved] 2nd Period (2012.03.01 ~ 2013.02.28) + A framework for recommender system The Final Goal = ‣ Investigate New recommendation Method (IEC + Content-based filtering) ‣ Validate proposed method ‣ Apply advanced method (e.g., multi-objective concept & genetic programming) ‣ Validate proposed method 8

9. Preliminary Experiment

10. The experiment is designed based on my previous work [1]. ‣ apply evolutionary approach (i.e., genetic algorithm) ‣ compare my designed system with exiting system [2] 10 ‣ Criteria (To minimize) Average Similarity Value [1] A Recommender System Based on Genetic Algorithm for Music Data, 2010 2nd International Conference on Computer Engineering and Technology, Volume 6, P414-417, 2010 [2] K. Yoshii et al., An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model, Audio, Speech, and Language Processing, IEEE Transactions on 16, 435-447, 2008 References

11. Conclusion

12. In this research, I propose a new type of recommender system. This research is applicable to various fields as a promising recommendation technology. (e.g., web service, standalone application or mobile device) From experimental result, evolutionary approach can improve the recommendation quality. 12 Content-Based Filtering IEC ‣ By combining interactive evolutionary computation and the content-based filtering method

13. Question ?

14. Thank You for your attention !!