An Optimization of Collaborative Filtering Personalized
Recommendation Algorithm Based on Time Context Information
Xian Jin, Qin Zheng and Lily Sun
ICISO 2015, Toulouse
2015-03-20
Au t h o r s
Xian JIn
PhD. Candidate of Shanghai University of Finance and Ecnomics
Major in Management Science and Engineering
MBA and Software Engineering
Worked in Tencent（2 years）、Autodesk（6 years）.
Prof. Qing Zheng
vice-president of South University of Science and Technology of China (SUSTC)
CPC member, a doctorate degree holder, professor, and doctoral supervisor.
Dr. LiLy Sun
PhD. Candidate of Shanghai University of Finance and Ecnomics
Information and Communications Technology Research
The university of Reading
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Why we need personalized recommendation？
Where the application of personalized recommendation？
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CONTENTS
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How is the personalized recommendation works？
What I did in this paper?
About the Future
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Why
we need personalized recommendation？
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With the explosive growth of the Internet information, users facing
serious problems of information overload in big data times. The user
needs to spend a lot of time and effort to find useful information.
Base on this background, personalized recommendation system emerge
as the times require, and it can help users to acquire useful information
and knowledge from the massive information.
That’s why the personalized recommendation appears in our life.
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Where
the application of personalized recommendation？
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Personalized recommendation can be find in many e-commerce website.
Amazon
Inspired by your shopping trends
Inspired by your browsing history
Customers with similar searches purchased
Viewed this also viewed
Bought this also bought
Ultimately buy after viewed this
Customers who bought items in your cart
also bought
Products with similar tags
Today’s recommendations for you
New for you
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And some movie discover website.
SynopsiTV:
Personalized movie recommendation platform
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And some daily living website.
Hunch local(LB)
• Search results
• Like
• Dislike
• Unique V.S popular
• Question based
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There are lots of models and algorithms
in the recommendation system.
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How
is the personalized recommendation works？
11.11
Single's Day
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After 2009-11-11, this day was re-defined
By Alibaba Inc.
11.11
Online Shopping Day = Black Friday online
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Single's Day
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11.11
What is the 57.112 billion means？
0.08% of Chinese GDP of Y2014
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How many sales of 11.11 comes from personal recommendation?
Personal recommendation Sales
in alibaba 11.11 (Billions)
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25.19
2014 Alibaba 11.11
Recommendation Sales percentage of
total sales:
30%
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Recommendation caused User number:
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92.61 Millon
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8.43
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0.5
0
2012
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2013
2014
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Here is the accuracy difference between four algorithms
Global ranking (Blue),
Collaborative filtering (yellow),
Heat conduction (purple)
Diffusion (red)
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In recommendation area, there mainly are ten hot research topics.
数据稀疏性问题
 The problem of sparse data
冷启动问题
 The problem of cold start
大数据处理与增量计算问题
 The big data processing and incremental calculation problem
多样性与精确性的两难困境
 The dilemma of the diversity and accuracy
推荐系统的脆弱性问题
 The vulnerability of recommendation systems
用户行为模式的挖掘和利用
 Mining and utilization of user behavior mode
推荐系统效果评估
 Recommendation system effectiveness evaluation
用户界面与用户体验
 The user interface and user experience
多维数据的交叉利用
 Cross utilization of multidimensional data
社会推荐
 Social recommendation
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I want do more research here，and try to improve the diversity and accuracy.
The dilemma of the diversity and accuracy
You know, recommend system was calculated based on the history data. So the recommend list must be the items which
user was like, most of research doing well in accruracy.
But human always like the fresh items, maybe something they never click\search\buy.
So a good recommend model should balance the diversity and accuracy.
Something like “Create a fresh accuracy requirement for Users”
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What
I did in this paper?
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In this paper I made some optimize on CF method.
What is the Collaborative filtering approach?
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Base on User
Base on Item
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CF method do have mainly four shortages.
（1）The problem of sparse data
（ 2）Method hardly to extend。
（ 3）The problem of cold start ，such as new users and new items。
（ 4）hardly to generate Fresh interest-------Scenarios
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Add time division to create a Users-Items-Time Matrix.
Time was divided as Monday ….. Sunday; Mornig-noon-evening…
User will get different recommend lists in different time.
This method can improve both the diversity and accuracy
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The result is better than normal method.
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About the future
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Time division is just a start…
Users….Items….Time…Location….Weather….Doing….Social connect….
The future is coming….
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