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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 魏志平(Chih-Ping Wei) | |
dc.contributor.author | Wen-Heng Qiu | en |
dc.contributor.author | 丘文恒 | zh_TW |
dc.date.accessioned | 2021-06-17T08:25:36Z | - |
dc.date.available | 2019-08-19 | |
dc.date.copyright | 2019-08-19 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-12 | |
dc.identifier.citation | Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. In Recommender systems handbook(pp. 217-253). Springer, Boston, MA.
Benghozi, P. J., & Salvador, E. (2015). Technological competition: a path towards commoditization or differentiation? Some evidence from a comparison of e-book readers. Systemes d'information management, 20(3), 97-135. Breese, J. S., Heckerman, D., & Kadie, C. (1998, July). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43-52). Morgan Kaufmann Publishers Inc.. Breitzman, A., & Thomas, P. (2015). The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems. Research policy, 44(1), 195-205. Cappelli, R., Corsino, M., Laursen, K., & Torrisi, S. (2018, July). Technological Competition & Patent Strategy: Protection, Blocking Rivals or the Freedom to Operate. In Academy of Management Proceedings (Vol. 2018, No. 1, p. 18067). Briarcliff Manor, NY 10510: Academy of Management. Cassiman, B., Colombo, M. G., Garrone, P., & Veugelers, R. (2005). The impact of M&A on the R&D process: An empirical analysis of the role of technological-and market-relatedness. research policy, 34(2), 195-220. Covington, P., Adams, J., & Sargin, E. (2016, September). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 191-198). ACM. Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., ... & Sampath, D. (2010, September). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems (pp. 293-296). ACM. Ding, Y., & Li, X. (2005, October). Time weight collaborative filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 485-492). ACM. Downie, J. S., Ehmann, A. F., Bay, M., & Jones, M. C. (2010). The music information retrieval evaluation exchange: Some observations and insights. In Advances in music information retrieval (pp. 93-115). Springer, Berlin, Heidelberg. Efimenko, I. V., & Khoroshevsky, V. F. (2014, September). New technology trends watch: An approach and case study. In International Conference on Artificial Intelligence: Methodology, Systems, and Applications (pp. 170-177). Springer, Cham. Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., & Zalányi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics, 95(1), 225-242. Fall, C. J., Törcsvári, A., Benzineb, K., & Karetka, G. (2003, April). Automated categorization in the international patent classification. In Acm Sigir Forum (Vol. 37, No. 1, pp. 10-25). ACM. Hasan, M., Ahmed, S., Malik, M. A. I., & Ahmed, S. (2016, December). A comprehensive approach towards user-based collaborative filtering recommender system. In 2016 International Workshop on Computational Intelligence (IWCI) (pp. 159-164). IEEE. Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53. Huang, Y., Zhu, D., Qian, Y., Zhang, Y., Porter, A. L., Liu, Y., & Guo, Y. (2017). A hybrid method to trace technology evolution pathways: a case study of 3D printing. Scientometrics, 111(1), 185-204. Kim, B., Gazzola, G., Yang, J., Lee, J. M., Coh, B. Y., Jeong, M. K., & Jeong, Y. S. (2017). Two-phase edge outlier detection method for technology opportunity discovery. Scientometrics, 113(1), 1-16. Lee, J., Kim, C., & Shin, J. (2017). Technology opportunity discovery to R&D planning: Key technological performance analysis. Technological Forecasting and Social Change, 119, 53-63. Lee, S., Yoon, B., & Park, Y. (2009). An approach to discovering new technology opportunities: Keyword-based patent map approach. Technovation, 29(6-7), 481-497. Lenox, M., & King, A. (2004). Prospects for developing absorptive capacity through internal information provision. Strategic management journal, 25(4), 331-345. Ma, J., & Porter, A. L. (2015). Analyzing patent topical information to identify technology pathways and potential opportunities. Scientometrics, 102(1), 811-827. Morrison, G., Riccaboni, M., & Pammolli, F. (2017). Disambiguation of patent inventors and assignees using high-resolution geolocation data. Scientific data, 4, 170064. Nguyen, K. L., Shin, B. J., & Yoo, S. J. (2016, January). Hot topic detection and technology trend tracking for patents utilizing term frequency and proportional document frequency and semantic information. In 2016 international conference on big data and smart computing (BigComp) (pp. 223-230). IEEE. Park, Y., & Yoon, J. (2017). Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering. Technological Forecasting and Social Change, 118, 170-183. Park, Y., & Yoon, J. (2017). Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering. Technological Forecasting and Social Change, 118, 170-183. Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer, Boston, MA. Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Www, 1, 285-295. Song, K., Kim, K. S., & Lee, S. (2017). Discovering new technology opportunities based on patents: Text-mining and F-term analysis. Technovation, 60, 1-14. Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009. Wang, J., & Chen, Y. J. (2017, July). Technological Opportunity Analysis for the Telehealth Industry. In 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 35-40). IEEE. Wang, W., Zhang, G., & Lu, J. (2015). Collaborative filtering with entropy‐driven user similarity in recommender systems. International Journal of Intelligent Systems, 30(8), 854-870. Wang, X., Ma, P., Huang, Y., Guo, J., Zhu, D., Porter, A. L., & Wang, Z. (2017). Combining SAO semantic analysis and morphology analysis to identify technology opportunities. Scientometrics, 111(1), 3-24. Wilding, R., Sharif, A. M., & Irani, Z. (2006). Applying a fuzzy‐morphological approach to complexity within management decision making. Management Decision. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82. Yoon, B., Park, I., & Coh, B. Y. (2014). Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining. Technological Forecasting and Social Change, 86, 287-303. Yoon, J., Park, H., Seo, W., Lee, J. M., Coh, B. Y., & Kim, J. (2015). Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework. Technological Forecasting and Social Change, 100, 153-167. Zheng, Z., Ma, H., Lyu, M. R., & King, I. (2009, July). Wsrec: A collaborative filtering based web service recommender system. In 2009 IEEE International Conference on Web Services (pp. 437-444). IEEE. Zwicky, F. (1969). Discovery, invention, research through the morphological approach. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74235 | - |
dc.description.abstract | 技術競爭被認為是數字時代競爭和創新戰略的關鍵維度。技術發展中最重要 的任務之一是在考慮到公司目前的限制因素(例如研發成本)下確定研發方向。
預測公司自身或競爭對手的技術發展,可以提高公司的研發實用性和表現, 同時降低研發投資風險水平。結合企業常規研發計畫和技術發展預測,企業可以 盡量避免研究與開發的盲點。企業還可以利用技術發展預測來尋找潛在技術互補 領域的企業,以最大化合併或收購的效益。此外,它還可以預先通過取代替代發 明(圍欄策略)以阻止競爭對手的商業活動,並避免技術被其他競爭者所擁有的 風險,或作為訴訟和交叉許可(遊戲策略)討價還價的籌碼。 本文提出了一種旨在支持公司技術發展預測的基於技術軌跡的協同過濾算 法,並獲得優於性能基準的表現。研究主要將公司的研究順序考慮在內,並嘗試 探索不同相似度計算方法及其對應的推薦質量。這種方法僅需使用公開的專利數 據,便可以簡單易行的方式預測公司自身或競爭對手的研究方向。因而具有易於 實作,低成本的特點,並可作為傳統的基於文本挖掘方法的補充。 | zh_TW |
dc.description.abstract | Technological rivalry is recognized as a key dimension of competition and innovation strategies in the digital era. One of the most important tasks in the technological development is determining the R&D directions, taking into consideration the firms’ present constraints, such as the costs of R&D.
Forecasting a firm’s own or competitors’ technological development, can improve a firm’s R&D practicality and performance while reducing the level of R&D investment risk. Moreover, combining original scientific effort and technological development forecasting, a firm can try the best to avoid blind spot of research works. A firm can also take the advantage of technological development forecasting to merge or acquire other companies that are active in complementary technological fields. In addition, it can also block the commercial endeavors of rivals and by preempting substitute inventions (fence strategy), to avoid the risk of hold-up by other technology owners, or as a bargaining chip in litigation and cross-licensing (play strategy). In this paper, we present a technological trajectory-based collaborative filtering algorithm to support firm technological development forecasting, which outperforms the performance benchmarks. We take a firm’s research order into account and attempt to explore different values of Firm-IPC table and its corresponding quality of recommendations. This approach can give a simple and easy-to-implement way to forecast a firm’s own or competitor’s research orientation by only using publicly available patent data. It is also an efficient, low-cost way for complementing the traditional text mining-based approach. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:25:36Z (GMT). No. of bitstreams: 1 ntu-108-R06725051-1.pdf: 1618265 bytes, checksum: 5e2d5c4f08b1831aca224e3e22a2f10c (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii Table of Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Motivation and Objectives 2 Chapter 2 Literature Review 4 2.1 Technological Opportunity Discovering 4 2.1.1 Morphological Analysis-Based Approach 4 2.1.2 Text Mining Based Approach 5 2.2 Emerging Technology and Technology Trend Detection 6 2.3 Technological Development Forecasting 8 Chapter 3 Methodology 10 3.1 International Patent Classification 11 3.2 Co-rated Concept 11 3.3 Content Similarity Calculation 12 3.3.1 Patent Size Method 12 3.3.2 Five Scale Method 13 3.3.3 Binary Method 13 3.4 Order Similarity Calculation 13 3.4.1 Cosine Similarity 13 3.4.2 Pearson Correlation Coefficient 14 3.4.3 Overall Similarity 14 3.5 Collaborative Filtering Algorithm 15 Chapter 4 Empirical Evaluation 16 4.1 Patent Documents Collection 16 4.2 Metrics for Experimentation 19 4.2.1 Coverage 20 4.2.2 Precision 20 4.2.3 Recall 20 4.2.4 Mean Reciprocal Rank 20 4.3 Parameter tuning experiment 21 4.4 Experimental Evaluation 23 4.5 Error Analysis 25 4.6 Illustration of Case 25 4.7 Summary 26 Chapter 5 Conclusions 28 5.1 Contributions 28 5.2 Future Work 29 Reference 30 Appendix A 35 | |
dc.language.iso | zh-TW | |
dc.title | 基於軌跡的協同過濾方法支持企業技術發展預測 | zh_TW |
dc.title | A Trajectory Based Collaborative Filtering Approach to Support Firm Technological Development Forecasting | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 簡立峰,楊錦生 | |
dc.subject.keyword | 技術軌跡,研發,協同過濾,技術發展預測, | zh_TW |
dc.subject.keyword | technological trajectory,R&D,collaborative filtering,technological development forecasting, | en |
dc.relation.page | 36 | |
dc.identifier.doi | 10.6342/NTU201903093 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-13 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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