About Our Recommender System


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The material presented at the spring session of Operations Research Society Japan.

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About Our Recommender System

  1. 1. About Our Recommender System Kimikazu Kato, Chief Scientist Silver Egg Technology Co., Ltd.
  2. 2. Table of Contents• About myself• About the company and its business• Survey on related researches• Conclusion 1
  3. 3. About MyselfKimikazu Kato• Ph.D in computer science, background in mathematics• Joined Silver Egg as a Chief Scientist in Nov. 2012• Experiences in numerical computation – 3D CAD, geometric computation – Computer graphics – Partial differential equation – Parallel computation, GPGPU• Now designing the core of recommender system 2
  4. 4. About Silver Egg Technology Silver Egg Technology Book written by CEO Established: September, 1998 CEO: Tom Foley ≪著書≫「One to Oneマーケティングを超えた COO: Junko Nishimura 戦略的Webパーソナライゼーション」 (出版社:日経BP社 発売:2002年5月) Capital: ¥78 Million Main Services: 「ASP・SaaS・ICTアウトソーシングアワード2009」 ASP・SaaS部門「委員長特別賞」受賞 Recommender System Online Advertisement Service 第8回(2010)、第9回(2011) 「デロイト 日本テクノノロジー Fast50」受賞 3
  5. 5. Recommender System Recommender system proposes the items best fit for individuals’ needs. Good recommender system provides a comfort for online shopping experiences and improves customer loyalty. Ranking No.1 No.2 No.3 XXXXXX XXXXXXXXX 3,800円 Combination Additional Cross-sell 4
  6. 6. Consistent behavior targetingConsistent user behavior targeting from “traffic inflow” to “retention” isessential for improving sales and profit. Pre-access On-access Post-access Traffic inflow Service Conversion Retention Recommender Aigent Mail Aigent HotView Aigent Aigent Gadget Aigent On- Personalized Aigent Transaction Recommender Event Driven Retargeting ad LPO Recommado Portal Demand Printing Search Recommender Mail Mail Mail Aigent Suite (Real Time Recommender Platform) Silver Egg Technology provides smart targeting technology which enables optimization of online marketing 5
  7. 7. Interaction of Advertisement and Recommender Media Dashboard Merchandizer -Registers items to promote Consumer - Checks performance Discovery in a media site Shows ads of items to promote to the target users HotView - Timestamp - Geographic information - Use behavior - Demands To the shopping site - Contexts (search words) 百貨店 AigentTo the site they are interested 通販カタログ Aigent Suite ブティック Recommendation for TVショッピング up-sell and cross-sell アパレル Retailer Ad contents based on users behaviors in shopping sites are more likely to attract attentions and effectively lead users back to those sites 6
  8. 8. Mechanism Aigent server Client’s EC site “Who bought what” Stored and analyzed “Who is browsing what” Respond in real time “What should be recommended” ASP service + Batch update of inventory Code snippet to connect with AIgent Characteristics: • Real time response • Implemented as an add-on (cost efficient) 7
  9. 9. Consulting Services• Just showing the result of mathematical computation is not enough• To extract optimal sales, parameters should be tuned by hand – Statistical co-relation is not all that matters.• Sometimes recommendations should reflect some “intention” – According to policy, strategy, etc.• Continuous monitoring and A/B testing 8
  10. 10. About recommendation algorithms• Collaborative filtering• Fruitful methods as a result of Netfilx Prize – Neighborhood Models – Matrix factorization – Restricted Boltzmann Machines 9
  11. 11. Netflix Prize The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings — Wikipedia Netflix provided open data for this competition Closed in 2009 10
  12. 12. Movie Rating Prediction Each user gives rating to the movies they saw movie user W X Y Z A 5 4 1 4 B 4 C 2 3 D 1 4 ? Is it possible to predict the rating of unknown user/movie pair? Ratings are expressed as a sparse matrix. A zero value of the matrix doesn’t really mean “zero” but “unknown” 11
  13. 13. Probabilistic Matrix Factorization Regarding ratings are expressed by small number of components 𝐴 𝑈𝑇 𝑉 noise Approximate only the non-zero elements 12
  14. 14. According to Bayes’ theorem, Minimize this objective function 13
  15. 15. Rating vs Purchase Movie rating Purchase recommendation movie item user W X Y Z user W X Y Z A 5 4 1 4 A 1 1 1 1 B 4 B 1 C 2 3 C 1 D 1 4 ? D 1 1 ? Predicts the rating for the user and Predicts how likely the user buy the movie pair. item The matrix includes negative feedback No negative feedback (Some movies are rated as “boring”) (No reason is given for missing elements) => Strong bias toward 1 Only one kind of value for known elements => Gives more degree of freedom A method successful in movie rating prediction is not useful for recommendation of usual shopping site. 14
  16. 16. Solutions• Regard a zero element as a negative feedback – Too ad hoc but better than naïve PMF• Assume a certain ratio of zero elements becomes one at the optimum [Sindhwani et al. 2010] – Assign other variables to zero elements and solve a relaxed optimization – Experimentally outperform the “zero-as-negative” method. V.Sindhwani et al., One-Class Matrix Completion with Low-Density Factorizations. In Proc. of ICDM 2010: 1055-1060 15
  17. 17. MinimizeSubject to: Solve this relaxed problem for non-negative variables 16
  18. 18. Conclusion• Scientific approach is important – Math really makes money• But that alone is not enough for real business• Engineering matters – Efficient platform and easy-to-deploy mechanism• Hand tuning part always remains – Consulting for parameter tune is essential 17