1. Authors: Rosario Sotomayor, Joe Carthy and John Dunnion Speaker: Rosario Sotomayor Intelligent Information Retrieval Group (IIRG) UCD School of Computer Science and Informatics University College Dublin Ireland The IIRG Group University College Dublin The Design and Implementation of an Intelligent Online Recommender System

2. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

3. University College Dublin The IIRG Group University College Dublin The IIRG Group What is a Recommender System? - Computer-based intelligent technique - Manages Information Overload - Used to efficiently provide personalized services in most e- commerce domains - Supports a customization of the customer experience through the representation of the products sold on a website - Enables the creation of a virtual world store personally designed for each customer The Goals of a Recommender System The Goals of a Recommender System: - Generate suggestions about new items - Predict the usefulness of a specific item for a particular user An overview of Recommender Systems

4. University College Dublin The IIRG Group University College Dublin The IIRG Group Recommender Systems in research system: Recommender Systems in research system : - - GroupLens - - Movielens Recommender Systems in commercial use : - Amazon.com - CDNOW - Pandora - Media Unbound An overview of Recommender Systems

5. University College Dublin The IIRG Group University College Dublin The IIRG Group Amazon.com An overview of Recommender Systems

6. University College Dublin The IIRG Group University College Dublin The IIRG Group Pandora: An overview of Recommender Systems

7. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

8. University College Dublin The IIRG Group University College Dublin The IIRG Group Collaborative Filtering (CF): Collaborative Filtering (CF): A promising Recommender System technology. Used in many of the most successful Recommender Systems on the web Collaborative filtering (CF) X X w y m r f c

9. University College Dublin The IIRG Group University College Dublin The IIRG Group Consists of a number of Sub-Tasks: - Representation - Neighborhood formation - Recommendation generation Applications of CF Applications of CF: - E-commerce : - Amazon.com (item-to-item collaborative filtering) - CDNow Limitations Limitations: - Scalability - Sparsity Collaborative filtering (CF)

10. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

11. University College Dublin The IIRG Group Singular Value Decomposition (SVD) Singular Value Decomposition (SVD): Dimensionality reduction technique Filters the useful data from the noise in large data sets. Applications Applications: - Information retrieval: Latent Semantic Indexing (LSI) - Recommender systems - Real-time signal processing - Seismic reflexion tomography Latent Semantic Indexing (LSI): Latent Semantic Indexing (LSI): Synonymy - Synonymy: “There are many ways to refer to the same object” - Polysemy - Polysemy: “Most words have more than one distinct meaning” Singular Value Decomposition (SVD)

12. University College Dublin The IIRG Group XT0T0 S0S0 D0D0 t x d t x r r x r r x d ·· = terms documents 0 0 Singular Value Decomposition (SVD) X = T0 · S0 · D0

13. University College Dublin The IIRG Group Singular Value Decomposition (SVD) X = T0 · S0 · D0 Where Where: T0, D0 = orthogonal matrices r= rank of the matrix X S = diagonal matrix = singular values of matrix X

14. University College Dublin The IIRG Group S0S0 0 0 Singular Value Decomposition (SVD) interesting evidence of latent structure noise, coincidences, anomalies, …

15. University College Dublin The IIRG Group XT0T0 S0S0 D0D0 t x d t x r r x r r x d ·· = terms documents 0 0 Singular Value Decomposition (SVD) X = T0 · S0 · D0

16. XT S D t x d t x k k x k k x d ··  terms documents q 0 0  T 0 ·S·D 0 = X  T·S·D Singular Value Decomposition (SVD) University College Dublin The IIRG Group

17. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

18. University College Dublin The IIRG Group Scenario Scenario: - Customers and their sets of products Dimensionality reduction technology Dimensionality reduction technology : - Singular Value Decomposition (SVD) : - Obtain less noisy reduced orthogonal dimensions - To capture latent relationships between customers and products Collaborative filtering: - To retrieve relevant information An SVD-CF Approach in the Recommender System Domain

19. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

20. The Intelligent Online Recommender System (IORS) goals Reduce the Sparsity Improve the quality of feedback Retrieval Time Reduction: - Timely feedback Search Shaping: - Anticipate user wishes - Reduce the noise generated by large quantities of data - Support the user in the process of selection University College Dublin The IIRG Group

21. The Intelligent Online Recommender System (IORS) goals Unveiling of New Preferences Unveiling of New Preferences: - Customers can take advantage of new relationships among users and products. Interactive GUI feedback: - Filters in different fashions University College Dublin The IIRG Group

22. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

23. The IORS Interface University College Dublin V

24. The IORS Interface University College Dublin The IIRG Group

25. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

26. The IORS Architecture University College Dublin The IIRG Group

27. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

28. Testing Evaluation Current testing is being done in order to measure the accuracy of SVD-CF methods. In order to do so, real data in sufficient quantity is being collected University College Dublin The IIRG Group

29. University College Dublin The IIRG Group An overview of Recommender Systems Collaborative Filtering (CF) Singular Value Decomposition (SVD) An SVD-CF Approach in the Recommender Systems Domain The IORS System Goals The IORS Interface The IORS Architecture Testing Evaluation Conclusions/Further work Outlines

30. CF is one of the most successful recommender system technologies, widely popular among e-tailers sites Recommender system technologies have become stretched by the huge volume of user information and are becoming even more stretched with the growth of Internet domain SVD plays a key role in the recommendation process of our system by addressing the gap left by collaborative filtering during the processing of high quantities of data It is important for SVD method that the derived k-dimensional factor space does not reconstruct the original term space perfectly, since the original set is deemed to be unreliable University College Dublin The IIRG Group

31. Further testing is required to understand the different results found when the k factor varies Further work is required to exploit SVD for item selection in order to find possible hidden relations among items University College Dublin The IIRG Group Conclusions/Further work

32. The End www.cs.ucd.ie Thank you! University College Dublin The IIRG Group