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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林守德(Shou-De Lin) | |
| dc.contributor.author | Kai-Wen Cheng | en |
| dc.contributor.author | 鄭凱文 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:43:46Z | - |
| dc.date.available | 2021-08-16 | |
| dc.date.available | 2022-11-24T03:43:46Z | - |
| dc.date.copyright | 2021-08-16 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-05 | |
| dc.identifier.citation | H. Abdollahpouri, R. Burke, and B. Mobasher. Managing popularity bias in recommender systems with personalized reranking, 2019. R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD ’93, page 207–216, New York, NY, USA, 1993. Association for Computing Machinery. Apple Inc. Apple App Store. https://www.apple.com/app-store/. Apple Inc. Apple Search Ads. https://searchads.apple.com/. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3(null):993–1022, Mar. 2003. J. Devlin, M. Chang, K. Lee, and K. Toutanova. BERT: pretraining of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805, 2018. Google. Google Play. https://play.google.com/. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009. T. Landauer, P. Foltz, and D. Laham. An introduction to latent semantic analysis. Discourse Processes, 25:259–284, 1998. Z. Li, J. Wu, L. Sun, and T. Rong. Combinatorial keyword recommendations for sponsored search with deep reinforcement learning, 2019. J. Lv, B. Song, J. Guo, X. Du, and M. Guizani. Interestrelated item similarity model based on multimodal data for topn recommendation. CoRR, abs/1902.05566, 2019. T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. In Y. Bengio and Y. LeCun, editors, 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 24, 2013, Workshop Track Proceedings, 2013. M. A. Qureshi and D. Greene. EVE: explainable vector based embedding technique using wikipedia. CoRR, abs/1702.06891, 2017. S. Rendle, C. Freudenthaler, Z. Gantner, and L. SchmidtThieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the TwentyFifth Conference on Uncertainty in Artificial Intelligence, UAI ’09, page 452–461, Arlington, Virginia, USA, 2009. AUAI Press. S. Robertson and H. Zaragoza. The probabilistic relevance framework: Bm25 and beyond. Found. Trends Inf. Retr., 3(4):333–389, Apr. 2009. G. Salton and C. Buckley. Termweighting approaches in automatic text retrieval. Information Processing Management, 24(5):513–523, 1988. F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang. Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. CoRR, abs/1904.06690, 2019. T. Wang and Y. Fu. Itembased collaborative filtering with BERT. In Proceedings of The 3rd Workshop on eCommerce and NLP, pages 54–58, Seattle, WA, USA, July 2020. Association for Computational Linguistics. J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. pages 2764–2770, 01 2011. T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush. Transformers: Stateoftheart natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online, Oct. 2020. Association for Computational Linguistics. X. Yang, T. Deng, Z. Guo, and Z. Ding. Advertising keyword recommendation based on supervised link prediction in multirelational network. WWW ’17 Companion, page 863–864, Republic and Canton of Geneva, CHE, 2017. International World Wide Web Conferences Steering Committee. Y. Zhang, W. Zhang, B. Gao, X. Yuan, and T.Y. Liu. Bid keyword suggestion in sponsored search based on competitiveness and relevance. Information Processing Management, 50(4):508–523, 2014. J. Zhao, G. Qiu, Z. Guan, W. Zhao, and X. He. Deep reinforcement learning for sponsored search realtime bidding. CoRR, abs/1803.00259, 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81333 | - |
| dc.description.abstract | 關鍵字推薦為贊助搜尋廣告系統中重要的一環，過去不少論文嘗試改善關鍵字的推薦品質，然而大部方法皆站在平台方的角度來設計、模型仰賴大量唯有平台方才能獲得的數據進行推薦，導致這些方法無法複製其結果於廣告主方的推薦上。本篇論文特別著重於廣告主一方，提出一個應用於手機應用程式廣告平台的關鍵字推薦系統，透過直接利用平台業者提供的搜尋排名，僅需單一廣告主的資料，便可獲得準確的應用程式和關鍵字的關聯性，再透過協同過濾方法達到高品質的推薦。實驗證明我們的方法能有效克服廣告主資料不足的問題，並在冷啟動和後續推薦階段上都能勝過其他方法。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:43:46Z (GMT). No. of bitstreams: 1 U0001-1807202109362700.pdf: 1604954 bytes, checksum: 06e33bdfae2f4a1f35bf213c5752a773 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 i 中文摘要 ii Abstract iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Related work 6 2.1 Trend-based approach 6 2.2 Content-based approach 7 2.3 Collaborative filtering approach 9 Chapter 3 Methodology 10 3.1 Platform organic search-ranking collection 10 3.2 User-Item matrix generation through search-ranking 12 3.3 Keyword ranking by all-pairs user-item scores 15 3.3.1 SR.All_Apps.Rule: Rule-based model for cold-start stage 15 3.3.2 SR.All_Apps.Learn: Learning-based model for following stage 16 Chapter 4 Experiment 20 4.1 Setup 20 4.1.1 Environment 20 4.1.2 Testing model list 21 4.1.3 Hypotheses 22 4.1.4 Metrics 22 4.2 Comparison between Search-ranking and Bert semantic models 23 4.3 Comparison between CF and non-CF models 24 4.4 Comparison between rule-based and learning-based models 25 4.5 Comparison between single SR and ensemble models 25 4.6 Comparison among different search-ranking scoring strategies 26 4.7 Summary 28 Chapter 5 Conclusion 29 References 30 | |
| dc.language.iso | en | |
| dc.subject | 協同過濾 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 贊助搜尋 | zh_TW |
| dc.subject | 關鍵字推薦 | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | Collaborative filtering | en |
| dc.subject | Keyword Recommender | en |
| dc.subject | Sponsored search advertising | en |
| dc.subject | Recommender system | en |
| dc.title | 面向廣告主的贊助搜尋關鍵字推薦系統 | zh_TW |
| dc.title | Keyword Recommender for Sponsored Search Advertising on Advertiser Side | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李宏毅(Hsin-Tsai Liu),孫民(Chih-Yang Tseng),葉哲華 | |
| dc.subject.keyword | 機器學習,推薦系統,贊助搜尋,關鍵字推薦,協同過濾, | zh_TW |
| dc.subject.keyword | Machine learning,Recommender system,Sponsored search advertising,Keyword Recommender,Collaborative filtering, | en |
| dc.relation.page | 32 | |
| dc.identifier.doi | 10.6342/NTU202101543 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-05 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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