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王擎
[email protected]
[email protected]
Hi: wangqing09hi
2014-4-1
RTB 简介
• Real-Time-Bidding 实时竞价购买广告的方式
• AdExchange 广告交易市场
• DSP(Demand Side Platform) 需求方平台,为广告主
的推广campaign优化投放
• SSP(Supply Side Platform) 供应方平台,为媒体(广
告位)进行服务管理
• DMP(Data Manage Platform) 数据管理平台,管理
不同来源的数据
RTB 简介
Picture from: http://contest.ipinyou.com/manual.shtml
RTB 简介
Picture from: http://contest.ipinyou.com/manual.shtml
RTB 特点
• 建立统一市场环境,角色细分
• (对于广告主) 网络媒体包买广告位
-> 每个曝光单独购买
• (对于媒体网站)提供广告位&观众的
交易平台,有效利用资源
advertisement
audience
iPinYou
• DSP:
• 人群分类(Audience Targeting)
– Cookie(1x1 pixel), AdEx Cookie Mapping, ...
– > User Tags
• 实时交易(RTB)
– 基础架构 ~100ms
– Bidding Model
iPinYou
拍卖规则
• 二价模型(Vickrey Auction or VCG)
• Highest price wins, pays 2nd-highest price.
• 鼓励真实出价
"The dominant strategy in a Vickrey auction with a single,
indivisible item is for each bidder to bid their true value of the
item."
cf. wikipedia: Vickrey_auction
模型目标
Maximize
Nclicks + N * Nconversions
subject to the fixed ad budget.
budget -> impression -> click/conv
CPI, CTR -> CPC
比赛流程
• 3个赛季
• 第二赛季:
1.线下leaderboard (6月-9月)
选出前5名
2.上线前调试
3.正式比赛3天 (9月中旬)
最终排名
• 3个Ader
• 4个AdExchange
Google Ali tencent Baidu
->
->
赛季结果
score = clicks + N * reaches
Team
Score
N=20
Team
CAS_MLRush
8429
o_o
4289
UCL-CA
1304
the9thbit
8229
梦想照进现实
4067
V_V
983
newline-CA
6844
UCL-CA
1995
Run Fast
901
Again
1409
PoundsXXX
885
deep_ml
1148
Tiger
744
http://contest.ipinyou.com/message_list.shtml
Score
N=6
Team
Score
N=1
Our Approach
1. Feature Extraction
2. CTR Prediction
3. Online Bidding
Features -> pCTR -> bid_price
Feature Extraction
• low-level + high-level
• CreativeId, AderId, AdExchange(Platform),
Domain, AdslotId, AdslotWidth, AdslotHeight,
AdslotVisibility, AdslotFormat, Floorprice,
UserTags, OS, IE(Browser), Region, City,
Weekday, Hour
Feature Extraction
adslot
user
ad
pCTR: Logistic Regression
Algorithm
J.Langford, L.Li, T.Zhang, Sparse Online Learning via Truncated Gradient
Vowel Wabbit https://github.com/JohnLangford/vowpal_wabbit/
Also cf. C.Perlich et al. Machine learning for targeted display advertising:
Transfer learning in action (SGD + elastic net)
Algorithm
Bidding
Bid price:
β 约为 2.0 .. 3.0
Also cf. Season1江申的分享, 及Media6Degrees : Bid Optimizing and
Inventory Scoring in Targeted Online Advertising (Step function)
“Any ratio below 0.8 yields a bid price of 0 (so not bidding), ratios
between 0.8 and 1.2 are set to 1 and ratios above 1.2 bid twice the base
price.”
Offline results
Offline results
Online results (Sep.16, Ader 2821)
Online results (Sep.16, Ader 2940)
Online results (Sep.16, Ader 3430)
遇到的挑战
• 预估CTR
1. 样本不平衡([LR in rare events])
2. 特征稀疏(reg, naive bayes)
3. 算法的收敛(2nd order / 1st order)
• 控制花钱速度
1. 流量预测 (at different bid levels)
2. 市场竞争环境建模(Bid landscape forecasting?)
3. 自动调节
预算控制 K.Lee, A.Jalali, A.Dasdan, Real Time Bid Optimization with Smooth
Budget Delivery in Online Advertising
实际系统
• 算法:
1. 人群分类(user tags)
2. 广告预选 (rank)
3. 在线反馈 (online learning)
4. 冷启动/热启动 (transfer learning)
• 架构:
1. 实时响应 并发
2. 数据日志整合
3. 其他
总结与收获
1. Feature Extraction
low-level + high-level
2. CTR Prediction
logistic regression
3. Online Bidding
power scheme
• 学习的过程
• 实践的机会
• 交流的平台
Thanks!
参考
• 比赛官网 http://contest.ipinyou.com/
• 数据下载 http://pan.baidu.com/s/1kTkGUQN
• J.Langford, L.Li, T.Zhang, Sparse Online Learning via
Truncated Gradient
• C.Perlich, B.Dalessandro, R.Hook, O.Stitelman, T.Raeder,
F.Provost, Bid Optimizing and Inventory Scoring in
Targeted Online Advertising
• K.Lee, A.Jalali, A.Dasdan, Real Time Bid Optimization
with Smooth Budget Delivery in Online Advertising
• C. Perlich, B. Dalessandro, O. Stitelman, T. Raeder, F.
Provost, Machine learning for targeted display
advertising: Transfer learning in action
参考
• Y.Cui, R.Zhang, W.Li, J.Mao, Bid Landscape Forecasting in
Online Ad Exchange Marketplace
• G.King, Logistic Regression in Rare Events
• 江申的分享 http://www.techinads.com/archives/41
http://pan.baidu.com/share/link?shareid=322913515&uk
=3138366223
• 品友有关DSP的介绍
• 艾瑞咨询2013年中国DSP行业发展报告

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