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E-raamat: Budget Constraints and Optimization in Sponsored Search Auctions

(Yanwu Yang is an Associate Professor at the Chinese Academy of Sciences. He is also an INFORMS member, and the Secretary of the ACM Chapter on Social and Economic Computing.), (National University of Defense Technology, Changsha, China)
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  • Ilmumisaeg: 23-Nov-2013
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780124115040
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Budget Constraints and Optimization in Sponsored Search AuctionsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 23-Nov-2013
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780124115040
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The Intelligent Systems Series publishes reference works and handbooks in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. They include theoretical studies, design methods, and real-world implementations and applications. The series' readership is broad, but focuses on engineering, electronics, and computer science.

Budget constraints and optimization in sponsored search auctions takes into account consideration of the entire life cycle of campaigns for researchers and developers working on search systems and ROI maximization. The highly experienced authors compiled their knowledge and experience to provide insight, algorithms and development techniques for successful optimized/constrained systems. The book presents a cutting-edge budget optimization approach that embraces three-level budget decisions in the life cycle of search auctions: allocation across markets at the system level, distribution over temporal slots at the campaign level, and real-time adjustment at the keyword level.

  • Delivers a systematic overview and technique for understanding budget constraints and ROI optimization in sponsored search auction systems, including algorithms and developer guides for a range of scenarios
  • Explores effects of constraints on mechanisms, bidding and keyword strategies, and the strategies for budget optimization that developers can employ
  • An informative reference source for both software and systems developers working in the search auctions, marketing and sales strategy optimization, services development for online marketing and advertisement, e-commerce, social and economic networking

Muu info

Delivers the algorithms and insight engineers and developers need to understand and maximize ROI on search activities
Preface xi
Chapter 1 Search Engine Meets Economics 1(12)
1.1 The Web and Search Engines
1(2)
1.1.1 The Web
1(1)
1.1.2 Search Engines
2(1)
1.2 Internet and Search Economics, Search Engine Marketing
3(2)
1.2.1 Internet and Search Economics
3(2)
1.2.2 Search Engine Marketing
5(1)
1.3 A First Glimpse of Sponsored Search Auctions
5(1)
1.4 Understanding the Budget in Sponsored Search Auctions
6(2)
1.5 How this Book is Organized
8(5)
1.5.1 Part I
8(1)
1.5.2 Part II
9(1)
1.5.3 Part III
9(1)
1.5.4 Part IV
9(1)
1.5.5 Part V
10(3)
Chapter 2 Budget Constraints in Sponsored Search Auctions 13(18)
2.1 Introduction
13(2)
2.2 Budget-Constrained Sponsored Search Auctions
15(9)
2.2.1 Budget-Centered Views
15(2)
2.2.2 Budget-Constrained Mechanisms
17(4)
2.2.3 Budget-Constrained-Bidding Strategies
21(3)
2.3 Summary
24(7)
Chapter 3 Budget Optimization in Sponsored Search Auctions 31(12)
3.1 Introduction
31(1)
3.2 Budget Decision Scenarios
31(2)
3.3 The Necessity of Budget Optimization
33(2)
3.4 A Budget Taxonomy in Sponsored Search Auctions
35(2)
3.5 Discussions
37(2)
3.6 Conclusions
39(4)
Chapter 4 A Budget Optimization Framework for Search Advertisements 43(20)
4.1 Introduction
43(2)
4.2 Challenges
45(1)
4.2.1 Problem Statement
45(1)
4.2.2 Definition
46(1)
4.3 A Budget Optimization Framework
46(2)
4.4 Mathematics of the Budget Optimization Framework
48(3)
4.4.1 Formulation
48(1)
4.4.2 Properties
49(2)
4.4.3 The Solution
51(1)
4.5 The Framework Instantiation
51(2)
4.5.1 Basis
51(1)
4.5.2 The Model
52(1)
4.6 Experiments and Validation
53(6)
4.6.1 Data Description
53(1)
4.6.2 Experimental Design
53(2)
4.6.3 Experimental Results
55(4)
4.7 Discussions
59(1)
4.8 Conclusions
60(3)
Chapter 5 The First Step to Allocate Advertising Budget in Sponsored Search Auctions 63(12)
5.1 Introduction
63(1)
5.2 Problem Statement
64(1)
5.3 Budget Initialization
65(2)
5.4 Budget Adjustment
67(3)
5.4.1 Parameters Estimation
67(2)
5.4.2 Model Formulation and Solution
69(1)
5.5 Experimental Validation
70(2)
5.5.1 Data Preparation
70(1)
5.5.2 Experimental Results
71(1)
5.6 Conclusions
72(3)
Chapter 6 Optimal Budget Allocation Across Search Markets 75(16)
6.1 Introduction
75(2)
6.2 Modeling Elements
77(1)
6.2.1 The Advertising Effort
77(1)
6.2.2 The Response Function for Search Advertisements
78(1)
6.3 Optimal Budget Allocation Across Search Markets
78(7)
6.3.1 The Model
78(1)
6.3.2 Properties
79(2)
6.3.3 The Solution
81(4)
6.4 Experimental Validation
85(4)
6.4.1 Data Descriptions
85(1)
6.4.2 Experimental Design
85(1)
6.4.3 Experimental Results
85(4)
6.5 Conclusions
89(2)
Chapter 7 Budget Allocation In Competitive Search Advertisements, Part I: In Static Environments 91(12)
7.1 Introduction
91(1)
7.2 The Budget Allocation Game
92(6)
7.2.1 The Complete-Information Budget Game
92(4)
7.2.2 The Incomplete-Information Budget Game
96(2)
7.3 An Evaluation Approach
98(1)
7.4 Experiments
99(1)
7.5 Conclusions
100(3)
Chapter 8 Budget Allocation in Competitive Search Advertisements, Part II: In Dynamic Environments 103(12)
8.1 Introduction
103(1)
8.2 Problem Statement
104(1)
8.3 Budget Competition
105(5)
8.3.1 The Response Function
105(1)
8.3.2 The Model
106(1)
8.3.3 Properties
107(3)
8.4 Experimental Evaluation
110(2)
8.4.1 Experimental Results
110(2)
8.4.2 Experimental Analysis
112(1)
8.5 Conclusions
112(3)
Chapter 9 Stochastic Budget Strategies at the Campaign Level: A Preliminary Investigation 115(14)
9.1 Introduction
115(2)
9.2 Problem Statement
117(1)
9.3 Budget Distribution Over Time
118(5)
9.3.1 A Stochastic Strategy
118(3)
9.3.2 The Advertising Utility
121(1)
9.3.3 Properties
122(1)
9.4 Experimental Evaluation
123(4)
9.4.1 Data Descriptions
123(1)
9.4.2 Experimental Design
124(1)
9.4.3 Experimental Results
125(2)
9.5 Conclusions
127(2)
Chapter 10 A Stochastic Budget Distribution Model in Search Advertisements 129(16)
10.1 Introduction
129(1)
10.2 A Stochastic Budget Distribution Model
130(2)
10.2.1 The Basic Model
130(1)
10.2.2 The Objective Function
131(1)
10.2.3 The Stochastic Model
132(1)
10.3 Properties and Solutions
132(7)
10.3.1 Uniform Distributed Budget
132(4)
10.3.2 Normal Distributed Budget
136(3)
10.4 Experiments
139(3)
10.4.1 Experimental Data
139(1)
10.4.2 Result Analysis
139(3)
10.4.3 Managerial Insights
142(1)
10.5 Conclusions
142(3)
Chapter 11 A Two-Stage Fuzzy Programming Approach for Budget Allocation in Sponsored Search Auctions 145(10)
11.1 Introduction
145(2)
11.2 Problem Formulation
147(3)
11.2.1 Problem Statement
147(1)
11.2.2 The Two-Stage Fuzzy Budget Allocation Model
148(2)
11.3 The Solution Algorithm
150(2)
11.3.1 The Recourse Function
150(1)
11.3.2 The PSO Algorithm
151(1)
11.4 Experiments
152(1)
11.5 Conclusions
153(2)
Chapter 12 Budget Planning for Coupled Campaigns in Sponsored Search Auctions 155(12)
12.1 Introduction
155(2)
12.2 Multi-Campaign Budget Planning
157(1)
12.2.1 The Three-Dimensional Relationship Between Campaigns
157(1)
12.2.2 The Ad Overlapping Degree
157(1)
12.2.3 The Model
158(1)
12.3 The Solution & Properties
158(2)
12.4 Experimental Validation
160(5)
12.4.1 The Ad Overlapping Degree
160(2)
12.4.2 The Budgeting Level
162(1)
12.4.3 The Overlapping Degree
163(1)
12.4.4 Management Insights
164(1)
12.5 Conclusions
165(2)
Chapter 13 Daily Budget Adjustment in Sponsored Search Auctions 167(10)
13.1 Introduction
167(1)
13.2 Real-Time Budget Adjustment
168(2)
13.2.1 The Model
168(1)
13.2.2 The Equivalent Model
169(1)
13.2.3 The Solution Algorithm
169(1)
13.3 Experiments
170(4)
13.3.1 Experimental Data
170(1)
13.3.2 Experimental Results
171(3)
13.3.3 Experimental Analysis
174(1)
13.4 Conclusions
174(3)
Chapter 14 Dynamic Budget Adjustment in Sponsored Search Auctions 177(14)
14.1 Introduction
177(1)
14.2 Problem Statement
178(1)
14.3 Dynamic Budget Adjustment
179(3)
14.3.1 The Budget Adjustment Model
179(2)
14.3.2 The Solution
181(1)
14.4 Experimental Evaluation
182(6)
14.4.1 Data Descriptions
182(2)
14.4.2 Experimental Results
184(4)
14.5 Conclusions
188(3)
Chapter 15 Perspectives: Looking into the Future of Budgeting Strategies in Sponsored Search Auctions 191(6)
15.1 Research Prospectives
191(2)
15.1.1 With Respect to Auction Mechanism
191(1)
15.1.2 With Respect to Advertising Strategy
192(1)
15.1.3 With Respect to Budget Optimization
192(1)
15.2 Joint Optimization of Advertising Strategies
193(4)
Index 197
Yanwu Yang is an Associate Professor at the Chinese Academy of Sciences. He is also an INFORMS member, and the Secretary of the ACM Chapter on Social and Economic Computing. Fei-Yue Wang is a State Specially Appointed Expert and Professor at the National University of Defense Technology and the Chinese Academy of Sciences. Currently Prof. Yang is the Editor-in Chief of the IEEE Intelligent Systems and IEEE transactions on Intelligent Transportation Systems. He is a fellow of IEEE, INCOSE, IFAC, ASME and AAAS.
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