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The material presented at the spring session of Operations Research Society Japan.
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Silver Egg Technology Co., Ltd.
About Our Recommender System
Kimikazu Kato, Chief Scientist
1
Table of Contents
• About myself
• About the company and its business
• Survey on related researches
• Conclusion
2
About Myself
Kimikazu 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
3
About Silver Egg Technology
≪著書≫「One to Oneマーケティングを超えた
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)
「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
第8回(2010)、第9回(2011) 「デロイト 日本テクノノロジー Fast50」受賞
Book written by CEO Silver Egg Technology
Established: September, 1998
CEO: Tom Foley
COO: Junko Nishimura
Capital: ¥78 Million
Main Services:
Recommender System
Online Advertisement Service
4
Ranking
Additional
Cross-sell
Combination
XXXXXX XXXXXXXXX 3,800円
Recommender System
No.1 No.2 No.3
Recommender system proposes the items best fit for individuals’ needs.
Good recommender system provides a comfort for online shopping
experiences and improves customer loyalty.
5
Aigent Mail
Aigent Personalized
Search Transaction
Mail Event Driven
Mail Recommender
Recommender
Aigent Recommender Recommado
Aigent LPO
Aigent Gadget Portal
HotView Retargeting ad
Aigent On-Demand Printing
Traffic inflow Service Conversion Retention
Pre-access On-access Post-access
Consistent behavior targeting
Consistent user behavior targeting from “traffic inflow” to “retention” is
essential for improving sales and profit.
Aigent Suite (Real Time Recommender Platform)
Silver Egg Technology provides smart targeting technology which enables optimization of online marketing
6
Media Dashboard
ブティック
百貨店
TVショッピング
通販カタログ
アパレル
Aigent Suite
Consumer
Merchandizer
Shows ads of items to promote to the target users
Retailer
Recommendation for up-sell and cross-sell
Discovery in a media site
- Timestamp - Geographic information - Use behavior - Demands - Contexts (search words) To the shopping site
To the site they are interested
HotView
Aigent
Interaction of Advertisement and Recommender
Ad contents based on users behaviors in shopping sites are more likely to attract attentions and effectively lead users back to those sites
-Registers items to promote - Checks performance
7
Mechanism
Client’s EC site
“Who bought what”
“Who is browsing what”
Aigent server
“What should be recommended”
Characteristics:
• Real time response
• Implemented as an add-on (cost efficient)
Code snippet to connect
with AIgent
+
Stored and analyzed
Respond in real time
ASP service Batch update of inventory
8
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
9
About recommendation algorithms
• Collaborative filtering
• Fruitful methods as a result of Netfilx Prize
– Neighborhood Models
– Matrix factorization
– Restricted Boltzmann Machines
10
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
11
Movie Rating Prediction
W X Y Z
A 5 4 1 4
B 4
C 2 3
D 1 4 ?
user
movie
Each user gives rating to the movies they saw
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”
12
Probabilistic Matrix Factorization
Regarding ratings are expressed by small number of components
noise
Approximate only the non-zero elements
𝐴 𝑈𝑇 𝑉
13
According to Bayes’ theorem,
Minimize this objective function
14
Rating vs Purchase
W X Y Z
A 5 4 1 4
B 4
C 2 3
D 1 4 ?
W X Y Z
A 1 1 1 1
B 1
C 1
D 1 1 ?
user
movie
user
item
Movie rating Purchase recommendation
Predicts the rating for the user and
movie pair.
Predicts how likely the user buy the
item
The matrix includes negative feedback
(Some movies are rated as “boring”) No negative feedback
(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.
15
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
16
Minimize
Subject to:
Solve this relaxed problem for non-negative variables
17
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
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