請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18205完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳彥仰(Mike Y. Chen) | |
| dc.contributor.author | Yi Huang | en |
| dc.contributor.author | 黃易 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:54:50Z | - |
| dc.date.copyright | 2015-03-16 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-02-24 | |
| dc.identifier.citation | [1] Bulletin board system (BBS). http://en.wikipedia.org/wiki/Bulletin_board_system.
[2] FFmpeg. http://www.ffmpeg.org/. [3] Globalmobilegamesrevenuesonnewzoo.http://www.newzoo.com/insights/global- mobile-games-revenues-top-25-billion-2014/. [4] Google Play. http://play.google.com/store/. [5] iTunes App Store. http://www.apple.com/itunes/charts/free-apps/. [6] Number of apps in the iTunes App Store on statista. http://www.statista.com/statistics/268251/number-of-apps-in-the-itunes-app- store-since-2008/. [7] I. Al Kabary and H. Schuldt. Enhancing sketch-based sport video retrieval by sug- gesting relevant motion paths. In Proceedings of the 37th international ACM SIGIR conference on Research & development in Information Retrieval (SIGIR ’14), pages 1227–1230, 2014. [8] T. H. Apperley. Genre and game studies: toward a critical approach to video game genres. In Simul. Gamin, pages 6–23, 2006. [9] E. Costa-Montenegro, A. B. Barragans-Martinez, and M. Rey-Lopez. Which app? a recommender system of applications in markets: Implementation of the service for monitoring users’ interaction. Expert Syst. Appl., 39(10):9367–9375, Aug. 2012. 29 [10] D. D. et al. A gameplay definition through videogame classification. In Int. J. Comput. Games Technol, volume 2008, page 4:1–4:7, 2008. [11] S. Genvo. Le game design de jeux vide ́o: Approches de l’expression vide ́oludique. l’harmattan. 2006. [12] W. Hu, N. Xie, L. Li, X. Zeng, and S. Maybank. A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(6):797–819, 2011. [13] H. Kubo, J. Pilet, H. Saito, and S. Satoh. Video retrieval based on tracked features quantization. In 2010 20th International Conference on Pattern Recognition (ICPR), pages 3248–3251. IEEE, 2010. [14] J. Lin, K. Sugiyama, M.-Y. Kan, and T.-S. Chua. New and improved: Modeling versions to improve app recommendation. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SI- GIR ’14), pages 647–656, 2014. [15] R. Marczak, J. van Vught, G. Schott, and L. E. Nacke. Feedback-based gameplay metrics: Measuring player experience via automatic visual analysis. In Proceed- ings of The 8th Australasian Conference on Interactive Entertainment: Playing the System (IE ’12), pages 6:1–6:10, 2012. [16] P.Mirza-Babaei,L.E.Nacke,J.Gregory,N.Collins,andG.Fitzpatrick.Howdoesit play better?: exploring user testing and biometric storyboards in games user research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13), pages 1499–1508, 2013. [17] C.-W. Su, H.-Y. Liao, H.-R. Tyan, C.-W. Lin, D.-Y. Chen, and K.-C. Fan. Mo- tion flow-based video retrieval. IEEE Transactions on Multimedia, 9(6):1193–1201, 2007. [18] K. R. W. Paireekreng and K. Wong. Personalised mobile game recommendation system. In GDTW 2008, 2008. 30 [19] M. J. Wolf. Genre and the video game. In The medium of the video game, pages 113–134, 2002. [20] B. Yan and G. Chen. Appjoy: Personalized mobile application discovery. In Pro- ceedings of the 9th International Conference on Mobile Systems, Applications, and Services, MobiSys ’11, pages 113–126, New York, NY, USA, 2011. ACM. [21] C. Yang, T. Wang, G. Yin, H. Wang, M. Wu, and M. Xiao. Personalized mobile application discovery. In Proceedings of the 1st International Workshop on Crowd- based Software Development Methods and Technologies, CrowdSoft 2014, pages 49–54, New York, NY, USA, 2014. ACM. [22] H.-T.Yang,D.-Y.Chen,Y.-X.Hong,andK.-T.Chen.Mobilegamerecommendation using touch gestures. In Proceedings of Annual Workshop on Network and Systems Support for Games (NetGames ’13), pages 5:1–5:6, 2013. [23] P. Yin, P. Luo, W.-C. Lee, and M. Wang. App recommendation: A contest between satisfaction and temptation. In Proceedings of the Sixth ACM International Confer- ence on Web Search and Data Mining, WSDM ’13, pages 395–404, New York, NY, USA, 2013. ACM. [24] V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang. Collaborative filtering meets mobile recommendation: A user-centered approach. In AAAI, volume 10, pages 236–241, 2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18205 | - |
| dc.description.abstract | 由於智慧型手機和平板的普及，行動平台上的遊戲成為成長最快速的產業之一。
僅僅在蘋果的 App Store 上面的遊戲就已經超過二十萬個了， 可以想見加上其他行動平台的遊戲，現在的行動遊戲數量有多驚人。 但是如同中央研究院資訊科學研究所的楊先生與他的夥伴們的研究論文所說的， 使用者很難用現有的系統找到符合他們需求的遊戲，尤其是和某個特定遊戲相似的遊戲。 本篇論文從現有的遊戲系統蒐集了 3456 個遊戲配對， 給 286 個使用者標記相似度之後，發現 Google Play 推薦的相似遊戲中， 平均有超過 80% 的遊戲被使用者認為是不相似的。 因此我們以質化訪談整理出使用者判定遊戲相似的依據， 並且提出根據這些相似因素可以大幅提昇相似遊戲推薦的準確率。 在本篇論文中，我們以從遊戲影片中擷取遊戲中的 motion vector 為例實作了一個相似遊戲推薦系統， 並且將準確度提昇了高達 42%。 | zh_TW |
| dc.description.abstract | Mobile gaming has become one of the fast growing industries due to the rise of the mobile market.
The number of mobile games has increased to over 200 thousands on App Store, not to mention there are still plenty of games on other platforms. However, as mentioned by Yang et al. in their work, current systems fail to provide sufficient support for users to find specific games that meet their needs, especially for similar games. The problem is also supported by our qualitative study and evaluation result. According to our similarity rankings collected from 286 people, current recommender system for similar games produces over 80% dissimilar games on average. We present a novel idea that by considering users definition of similarity between games, we can significantly improve recommendations for similar games by 42%. We demonstrate our idea by implementing a recommender system that incorporates motion features of gameplay video, which is one important similarity factor mentioned by our participants in the qualitative study. Our results show that by considering only gameplay motion features, our system can generate recommendations that outperform existing recommendations for similar games. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:54:50Z (GMT). No. of bitstreams: 1 ntu-104-R01922059-1.pdf: 4645335 bytes, checksum: a6fc569810e7c0cdd177459169ad7048 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 iii Abstract iv 1 Introduction 1 2 Related Work 3 3 Preliminary User Study 8 4 Dataset 13 5 Our Approach 18 6 Evaluation 23 7 Discussion 26 8 Conclusions 28 Bibliography 29 | |
| dc.language.iso | en | |
| dc.title | 以遊戲影片為例探討藉由使用者認知之相似因素改善手機遊戲推薦系統 | zh_TW |
| dc.title | Improving Recommendation of Similar Games by Incorporating Gameplay Video Features | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 范丙林,陳昇瑋(Sheng-Wei Chen),楊智傑(Chih-Chieh Yang) | |
| dc.subject.keyword | 推薦系統,行動遊戲(手機遊戲),相似遊戲,遊戲影片,使用者訪談, | zh_TW |
| dc.subject.keyword | Recommender systems,Mobile games,Similar games,Gameplay video,User study, | en |
| dc.relation.page | 31 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2015-02-24 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-104-1.pdf 未授權公開取用 | 4.54 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。