| 000 | 00000nam c2200205 c 4500 | |
| 001 | 000045932651 | |
| 005 | 20230712091423 | |
| 007 | ta | |
| 008 | 180102s2018 ulkad bmAC 000c eng | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 085 | 0 | ▼a 0510 ▼2 KDCP |
| 090 | ▼a 0510 ▼b 6D36 ▼c 1077 | |
| 100 | 1 | ▼a 박소정 ▼g 朴昭貞 |
| 245 | 1 0 | ▼a MoCA+ : ▼b incorporating user modeling into mobile contextual advertising / ▼d So Jung Park |
| 260 | ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2018 | |
| 300 | ▼a iv, 29장 : ▼b 삽화, 도표 ; ▼c 26 cm | |
| 500 | ▼a 지도교수: 이상근 | |
| 502 | 0 | ▼a 학위논문(석사)-- ▼b 고려대학교 대학원, ▼c 컴퓨터·전파통신공학과, ▼d 2018. 2 |
| 504 | ▼a 참고문헌: 장 27-29 | |
| 530 | ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf) | |
| 653 | ▼a Mobile Contextual Advertising ▼a In-app Advertising ▼a User Modeling | |
| 776 | 0 | ▼t MoCA+: Incorporating User Modeling into Mobile Contextual Advertising ▼w (DCOLL211009)000000080381 |
| 900 | 1 0 | ▼a Park, So Jung, ▼e 저 |
| 900 | 1 0 | ▼a 이상근, ▼g 李尙根, ▼d 1971-, ▼e 지도교수 ▼0 AUTH(211009)153285 |
| 945 | ▼a KLPA |
Electronic Information
| No. | Title | Service |
|---|---|---|
| 1 | MoCA+ : incorporating user modeling into mobile contextual advertising (41회 열람) |
View PDF Abstract Table of Contents |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Stacks(Thesis)/ | Call Number 0510 6D36 1077 | Accession No. 123058309 | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | Location Science & Engineering Library/Stacks(Thesis)/ | Call Number 0510 6D36 1077 | Accession No. 123058310 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Abstract
Mobile advertising accounts for more than half of digital ad investment and almost 20% of total ad spending on the market. It thus has become a significant source of revenue for mobile apps. Mobile contextual advertising is one of the recent approaches to improve the effectiveness of mobile advertising. However, we observe that there is still much room for improvement of current mobile contextual advertising platforms. In this thesis, we present a novel mobile contextual advertising platform, called MoCA+, which incorporates user modeling into an existing mobile contextual advertising platform. It is designed to provide contextual in-app ads relevant to both user interests and an app page content to third-party apps through its well-defined APIs. MoCA+ periodically collects and analyzes in-device user data, and models user interests in a background process. Then, when an application requests ads with their content, it captures the semantics of the content and provides semantically relevant ads to both user interests and an app page content. Because MoCA+ only works on mobile devices, there is no sending out in-device user data and an app page content outside the devices. It, therefore, protects user privacy. We evaluated the relevance of ads provided by MoCA+ and measured system overheads. Evaluation results confirm that MoCA+ effectively and efficiently supports mobile contextual advertising inside a mobile device. To the best of our knowledge, this is one of few works to implement the mobile contextual advertising platform without resort to servers.
Table of Contents
1 Introduction 2 Background 2.1 Mobile Advertising 2.2 ODP-based Contextual Advertising 3 MoCA+ 3.1 Design Goals 3.2 System Architecture 3.2.1 Tiny Text Intelligence 3.2.2 User Interest Manager 3.2.3 Ad Ranker 3.3 Workflow 4 Implementation 5 Evaluation 5.1 Relevance Performance 5.1.1 Evaluation Setup 5.1.2 Results 5.2 System Overheads 6 Related Works 7 Conclusions 7.1 Summary of This Thesis 7.2 Future Works
