政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/140664

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/140664

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/140664

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题名:	互動式主題標籤推薦系統 Interactive hashtag recommendation system
作者:	林俊廷 Lin, Chun-Ting
贡献者:	李蔡彥 Li, Tsai-Yen 林俊廷 Lin, Chun-Ting
关键词:	推薦系統自然語言處理社群媒體主題標籤 Recommendation system Natural language processing Social media Hashtag
日期:	2022
上传时间:	2022-07-01 16:21:47 (UTC+8)
摘要:	隨著網絡的不斷發展，越來越多的使用者將自己的所見所聞，透過推文（Tweet）的形式分享在社群媒體（Social Media）之中。這些推文以主題標籤（Hashtag）為聯結，在社群媒體中構成了許許多多的討論主題（Topic）。但由於大多數的使用者都沒有使用主題標籤的習慣，導致大量的推文無法被即時歸類到對應的主題，使得資訊呈現出離散的狀態。為了解決上述問題，本文提出了一種互動式主題標籤推薦系統，預測使用者所發推文的主題，以互動的方式推薦相關的主題標籤。此推薦系統可根據使用者的互動反饋，在編寫推文的不同階段提供適合的主題標籤，幫助社群形成主題共識，促進社群媒體意見的快速收斂。在實驗中，本研究邀請受試者使用此推薦系統，透過受試者的反饋來驗證系統的有用性。實驗結果顯示，本系統提出之互動式推薦流程可以幫助使用者找到適合推文主題的主題標籤。 With the progressive advance of Internet technologies, more and more users share their lives by posting tweets on social media platforms like Twitter. These tweets use hashtags as links to constitute discussion topics on social media. However, since most users are not used to using hashtags, a large number of tweets cannot be classified into corresponding topics immediately, which leads to a discrete state of information. To solve this problem, in this thesis, we propose an interactive hashtag recommendation system, which predicts the topic of an input tweet and interactively recommends rele-vant hashtags. This recommendation system can provide suitable hashtags in different phases of writing a tweet based on the interactive feedback of a user, help the commu-nity to reach a consensus, and increase the convergence speed of opinions on social media. We conducted user experiments to verify the usability of the recommendation system. The experimental results and user feedbacks reveal that the interactive hashtag recommendation can help users find suitable hashtags about the tweet’s topic.
參考文獻:	[1] S. M. Kywe, E.-P. Lim, and F. Zhu, "A survey of recommender systems in twitter," in Proceedings of the International Conference on Social Informatics, 2012: Springer, pp. 420-433. [2] H. Tsukayama, "Twitter turns 7: Users send over 400 million tweets per day," The Washington Post, vol. 21, 2013. [3] F. Godin, V. Slavkovikj, W. De Neve, B. Schrauwen, and R. Van de Walle, "Using topic models for twitter hashtag recommendation," in Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 593-596. [4] D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," arXiv preprint arXiv:1409.0473, 2014. [5] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin, "Attention is all you need," in Advances in neural information processing systems, 2017, pp. 5998-6008. [6] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018. [7] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," The Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003. [8] A. K. McCallum, "Mallet: A machine learning for language toolkit," http://mallet.cs.umass.edu, 2002. [9] X. Yan, J. Guo, Y. Lan, and X. Cheng, "A biterm topic model for short texts," in Proceedings of the 22nd international conference on World Wide Web, 2013, pp. 1445-1456. [10] C. Sievert and K. Shirley, "LDAvis: A method for visualizing and interpreting topics," in Proceedings of the workshop on interactive language learning, visualization, and interfaces, 2014, pp. 63-70. [11] K. Dey, R. Shrivastava, S. Kaushik, and L. V. Subramaniam, "Emtagger: a word embedding based novel method for hashtag recommendation on twitter," in 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017: IEEE, pp. 1025-1032. [12] E. Otsuka, S. A. Wallace, and D. Chiu, "Design and evaluation of a twitter hashtag recommendation system," in Proceedings of the 18th International Database Engineering & Applications Symposium, 2014, pp. 330-333. [13] T. Li, Y. Wu, and Y. Zhang, "Twitter hash tag prediction algorithm," in Proceedings on the International Conference on Internet Computing (ICOMP), 2011: Citeseer, p. 1. [14] B. Dhingra, Z. Zhou, D. Fitzpatrick, M. Muehl, and W. W. Cohen, "Tweet2vec: Character-based distributed representations for social media," arXiv preprint arXiv:1605.03481, 2016. [15] D. Kowald, S. C. Pujari, and E. Lex, "Temporal effects on hashtag reuse in twitter: A cognitive-inspired hashtag recommendation approach," in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1401-1410. [16] A. J. Lam and C. Cheng, "Utilizing Tweet Content for the Detection of Sentiment-Based Interaction Communities on Twitter," in 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018: IEEE, pp. 682-691. [17] A. Trotman, A. Puurula, and B. Burgess, "Improvements to BM25 and language models examined," in Proceedings of the 2014 Australasian Document Computing Symposium, 2014, pp. 58-65. [18] S. Humeau, K. Shuster, M.-A. Lachaux, and J. Weston, "Poly-encoders: Transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring," arXiv preprint arXiv:1905.01969, 2019. [19] N. Reimers and I. Gurevych, "Sentence-bert: Sentence embeddings using siamese bert-networks," arXiv preprint arXiv:1908.10084, 2019. [20] M. Kaviani and H. Rahmani, "Emhash: Hashtag recommendation using neural network based on bert embedding," in 2020 6th International Conference on Web Research (ICWR), 2020: IEEE, pp. 113-118. [21] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry," Communications of the ACM, vol. 35, no. 12, pp. 61-70, 1992. [22] Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, no. 8, pp. 30-37, 2009. [23] T. Miyanishi, K. Seki, and K. Uehara, "Improving pseudo-relevance feedback via tweet selection," in Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013, pp. 439-448. [24] L. Richardson, "Beautiful soup documentation," Dosegljivo: https://www.crummy.com/software/BeautifulSoup/bs4/doc/. [Dostopano: 7. 7. 2018], 2007. [25] M. Lui and T. Baldwin, "langid. py: An off-the-shelf language identification tool," in Proceedings of the ACL 2012 system demonstrations, 2012, pp. 25-30. [26] R. Řehůřek and P. Sojka, "Gensim—statistical semantics in python," Retrieved from genism. org, 2011. [27] S. E. Robertson and K. S. Jones, "Relevance weighting of search terms," Journal of the American Society for Information science, vol. 27, no. 3, pp. 129-146, 1976. [28] S. Robertson and H. Zaragoza, The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc, 2009. [29] J. Ramos, "Using tf-idf to determine word relevance in document queries," in Proceedings of the first instructional conference on machine learning, 2003, vol. 242, no. 1: Citeseer, pp. 29-48. [30] Y. Lv and C. Zhai, "Lower-bounding term frequency normalization," in Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 7-16. [31] F. Jian, J. X. Huang, J. Zhao, Z. Ying, and Y. Wang, "A topic‐based term frequency normalization framework to enhance probabilistic information retrieval," Computational Intelligence, vol. 36, no. 2, pp. 486-521, 2020. [32] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M.Lewis, L. Zettleoyer, and V. Stoyanov, "Roberta: A robustly optimized bert pretraining approach," arXiv preprint arXiv:1907.11692, 2019. [33] V. Sanh, L. Debut, J. Chaumond, and T. Wolf, "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter," arXiv preprint arXiv:1910.01108, 2019. [34] T. M. H. Reenskaug, "The original MVC reports," 1979. [35] M. Potel, "MVP: Model-View-Presenter the Taligent programming model for C++ and Java," Taligent Inc, p. 20, 1996. [36] E. Wilde and C. Pautasso, REST: from research to practice. Springer Science & Business Media, 2011. [37] A. Fedosejev, React. js essentials. Packt Publishing Ltd, 2015. [38] B. Efron and R. J. Tibshirani, An introduction to the bootstrap. CRC press, 1994. [39] B. Bibeault, A. De Rosa, and Y. Katz, jQuery in Action. Simon and Schuster, 2015. [40] J. J. Garrett, "Ajax: A new approach to web applications," 2005. [41] J. Brooke, "System usability scale (SUS): a quick-and-dirty method of system evaluation user information," Reading, UK: Digital Equipment Co Ltd, vol. 43, pp. 1-7, 1986. [42] R. Likert, "A technique for the measurement of attitudes," Archives of psychology, 1932. [43] A. M. Lund, "Measuring usability with the use questionnaire12," Usability interface, vol. 8, no. 2, pp. 3-6, 2001. [44] M. F. Porter, "Snowball: A language for stemming algorithms," ed, 2001. [45] A. Bangor, P. Kortum, and J. Miller, "Determining what individual SUS scores mean: Adding an adjective rating scale," Journal of usability studies, vol. 4, no. 3, pp. 114-123, 2009. [46] P. Virtanen, R. Gommers, T. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, "SciPy 1.0: fundamental algorithms for scientific computing in Python," Nature methods, vol. 17, no. 3, pp. 261-272, 2020.
描述:	碩士國立政治大學資訊科學系 109753208
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109753208
数据类型:	thesis
DOI:	10.6814/NCCU202200469
显示于类别:	[資訊科學系] 學位論文

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