II-SDV 2014 Recommender Systems for Analysis Applications (Roger Bradford - Agilex Technologies, USA)

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II-SDV 2014 Recommender Systems for Analysis Applications (Roger Bradford - Agilex Technologies, USA)

  1. 1. Recommender Systems for Analysis Applications Roger Bradford Agilex Technologies 14 April 2014 International Information Conference on Search, Data Mining and Visualisation
  2. 2. 2 • Customers who Shopped for ' A Tale of two Cities' also Shopped for …. • Customers Who Bought Items in Your Recent History Also Bought …. • Users who Enjoyed Titanic also Enjoyed …. Recommender Systems in Internet Commerce
  3. 3. 3 Provider Items Recommended Amazon Items to Buy FastWeb Scholarships LeShop Groceries to Buy Netflix Movies to Rent Pandora Music to Listen to Tripadvisor Places to Visit Twitter People to Follow YaHoo Movies to Watch Popular Commercial Recommender Applications
  4. 4. 4 • Business Strategy Development • Investment Analysis • Risk Analysis • IP Analysis • Fraud Detection • Event Monitoring • Technology Monitoring Example Analysis Applications In Analytic Applications, Recommender Systems Primarily Function as Knowledge Discovery Tools
  5. 5. 5 Value of Recommender Systems for Analysis Automatically Identify Important Information in Large Quantities of Incoming Data Reduce the Cognitive Load on Analysts Aid in Discovery of New Relevant Information - that the User didn’t Know to Search for Produce Alerts about Entities of Importance – not just more Documents to Read
  6. 6. 6 Typical Commercial Applications Typical Analytic Applications # of Users >> # of Items # of Items >> # of Users User Interests are Fairly Stable User Interests are Dynamic Unambiguous Indicators are Available Indicators are Mostly Subtle Missing a Recommendation Typically has Small Impact Missing a Recommendation may have a Large Impact Recommender Application Differences
  7. 7. 7 Approach Recommendations Based on • Collaborative Filtering Actions of Other People • Content-based Characteristics of Items • Demographic User Characteristics • Knowledge-based Example Cases or Constraints • Community-based Social Networks • Hybrid Combinations of the Above Implementation Approaches
  8. 8. 8 Incoming Reporting Stream Recommender Engine User-provided Exemplars Xxxxxxxxx Xxxxxxxxx .criminal Xxxxxxxxx ...crime.. Recommended Documents Recommended Entities User Action Artifacts Jason Brown Robert Fisher Walter Williams Analytic Recommendation Process
  9. 9. 9 Example User Interface Example Documents used to Define Interests Recommended Items in Relevance Order Confidence Indictors A 2011 report issued by the US Geological Survey and US Department of the Interior, "China's Rare-Earth Industry," outlines industry trends within China and examines national policies that may guide the future of the country's production. The report notes that China's lead in the production of rare-earth minerals has accelerated over the past two decades. In 1990, China accounted for only 27% of such minerals. In 2009, world production was 132,000 metric tons; China produced 129,000 of those tons. According to the report, recent patterns suggest that China will slow the export of such materials to the world: "Owing to the increase in domestic demand, the Government has gradually reduced the export quota during the past several years." I User Feedback Mechanism Exemplar Management Console
  10. 10. 10 Key Requirements for Analytic Recommenders Quickly Identify and Present Desirable Information to the User without Overwhelming the User with Irrelevant Information. Be Flexible Enough to Deal with Variability in Individuals and Activities Evaluate Complex Associations Based on Multiple Attributes (Including Metadata) Incorporate Data from Multiple Sources. Begin Making Recommendations Based on Small Amounts of Data
  11. 11. 11 Accommodate Data Volumes that can be Expected to be Very Large Deal with Data that is Sparse, Incomplete, and Noisy. Make Explanations of the Reasoning Used in Reaching the Recommendations Available to the User. Work with Data from Existing Corporate or Government Data Repositories. Key Requirements for Analytic Recommenders (Cont’d)
  12. 12. 12 • # of Items >> # of Users • Dynamic Items & User Interests • High Accuracy & Low Miss Rate Requirements Requirements Drive Implementation Approach Primary Recommendation Technique must be Content-based Matrix Factorization is the best Available Content-based Approach
  13. 13. 13 100 Million Ratings of 17,770 Movies by > 480,000 Users $1Million (US) Prize for 10% Improvement 44,000 Entries, From Over 41,000 Teams Won by Koren and Bell using a Combination of Techniques, Featuring Matrix Factorization The Netflix Challenge
  14. 14. 14 Matrix Factorization Advantages* Prediction Accuracy Superior to Other Techniques. Use of a Memory-efficient, Compact Model. Simple Training. Natural Ability to Integrate Multiple Forms of User Feedback. Ability to Incorporate Temporal Dynamics of User Interests and Item Attributes. No Reliance on Arbitrary or Heuristic Similarities. Inherent Protection against Overfitting. Ability to Capture the Totality of Weak Signals in the Data. Ability to Incorporate Confidence Levels. High Scalability. *Koren & Bell, Recommender Systems Handbook, Springer, 2011
  15. 15. 15 RecommendationAccuracy ComparedtoBaseline Degree of Text Corruption Noise Resilience
  16. 16. 16 Search Terms Viewing an Item Time Spent Viewing an Item Saving an Item Printing an Item Refining User Interests Explicit Input Implicit Indicators Exploit both Positive and Negative Indicators
  17. 17. 17 • Accuracy • Confidence Indicators for Recommendations • User Control • Explanation Contributors to User Confidence
  18. 18. 18 Explainability - Documents
  19. 19. 19 Explainability - Entities
  20. 20. 20 Lists Tables Text Analyst’s Notes: Identified Relevant Documents Documents In Novelty Order Previously Seen Information Published Reports Previously Reviewed Documents Novelty in Recommendations
  21. 21. 21 Crosslingual Recommendations Documents in Multiple Languages Farsi Arabic English Recommendations in Relevance Order Recommended Items
  22. 22. 22 Accuracy+Completeness ofCategorization Number of Simultaneous Languages English Documents & English Examples Documents in Latin Languages & English Examples Range of Human Performance High-Accuracy Multilingual Recommendations
  23. 23. 23 Multimedia Recommendations Integrated Semantic Analysis Structured Data Images Text Audio 8/18/02500 lbPicric Acid Saif al Adel Zaid Khayr DateAmountMaterialSellerBuyer Sensor Data Video Geospatial Data Biometrics
  24. 24. 24 High Performance with Modest HardwareTimeinHours Number of Documents K K KK K Minimum Latency – Single Processor Maximum Throughput – 16-node Hadoop Cluster
  25. 25. 25 Algorithm Scalability Conversational Recommender Systems Context-aware Recommenders Explanations and Evidence Preference Elicitation Privacy and Security Semantic Web Technologies for Recommendation Trust and Reputation Recommender Topics of Current High Interest
  26. 26. 26 The ACM Recommender System Conference (RecSys 2014), Foster City, California, USA, 6-10 October 2014 Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. Kantor, Springer Publishing, 2011 118€ Recommender Systems, P. Melville and V. Sindhwani, In Encyclopedia of Machine Learning, Springer, 2010. Available at: http://www.prem-melville.com/publications/recommender-systems- eml2010.pdf Matrix Factorization Techniques for Recommender Systems, Y. Koren, Y., R. Bell, and C. Volinsky, IEEE Computer, August 2009, pp. 42-49. Available at: http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf Resources
  27. 27. 27 Questions or Comments Roger Bradford Agilex Technologies Inc r.bradford@agilex.com

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