Responsive image
博碩士論文 etd-0729112-222447 詳細資訊
Title page for etd-0729112-222447
論文名稱
Title
根據資訊需求模式進行旅遊文章的推薦
Recommending Travel Threads Based on Information Need Model
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
54
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2012-07-02
繳交日期
Date of Submission
2012-07-29
關鍵字
Keywords
資訊需求模型、問題推薦、旅遊文章分類、文字分類、旅遊文章推薦
Text classification, Travel threads recommendation, Travel threads classification, Question recommendation, Information need model
統計
Statistics
本論文已被瀏覽 5888 次,被下載 1039
The thesis/dissertation has been browsed 5888 times, has been downloaded 1039 times.
中文摘要
推薦技術的主要目的是希望能夠在大量的資訊中發掘使用真正的資訊需求。
推薦系統幫助使用者過濾資訊以及嘗試呈現那些我們所感興趣的資訊供我們參考。
在本篇論文裡,我們專注在旅遊領域中的討論串(threads)推薦。我們考慮當使用
者有旅遊資訊需求的同時,他們會嘗試在網路上搜尋相關的資訊。除了瀏覽其他
使用者的建議或意見時,人們可能也會將真正的需求表達成一個問題,希望能夠
獲得其他人直接回覆。因此我們主要是根據使用者的瀏覽紀錄、輸入的問題以及
目前所處的需求階段來推薦相似的問題並且已經有相關答案的討論串給使用者。
我們提出了一個使用者模型,其中包含四個構面:目標相似度(goal similarity)、
內容相似度(content similarity)、時間(freshness)以及品質(quality)。我們希望整合
這四個構面可以提供有效的推薦結果。為了要驗證這四個構面以及推薦的績效,
我們從網路上最大的旅遊網站-TripAdvisor-收集了 14348 篇文章,以及徵募了 10
位對旅遊有興趣的自願者來做實驗。我們將這個四個構面分成兩個部分,第一個
部分為 Question-based 方法,涵蓋了三個構面,內容相似度(content similarity)、
時間(freshness)以及品質(quality),第二個部分是 Session-based 方法包含目標相
似度(goal similarity)。並且也將這兩個方法合併,提出混合方法(hybrid method)。
而實驗結果顯示混合方法(hybrid method)的結果比起兩個獨立方法可以提供相對
較好的推薦結果。
Abstract
Recommendation techniques are developed to discover user’s real information
need among large amounts of information. Recommendation systems help users filter
out information and attempt to present those similar items according to user’s tastes. In
our work, we focus on discussion threads recommendation in the tourism domain. We
assume that when users have traveling information need, they will try to search related
information on the websites. In addition to browsing others suggestions and opinions,
users are allowed to express their need as a question. Hence, we focus on
recommending users previous discussion threads that may provide good answers to the
users’ questions by considering the question input as well as their browsing records. We
propose a model, which consists of four perspectives: goal similarity, content similarity,
freshness and quality. To validate and the effectiveness of our model on
recommendation performance, we collected 14348 threads from TripAdvisor.com, the
largest travel website, and recruited ten volunteers, who have interests in the tourism, to
verify our approach. The four perspectives are utilized by two methods. The first is
Question-based method, which makes use of content similarity, freshness and quality
and the second is Session-based method, which involves goal similarity. We also
integrate the two methods into a hybrid method.
The experiment results show that the hybrid method generally has better
performance than the other two methods.
目次 Table of Contents
TABLE OF CONTENTS
CHAPTER 1 – Introduction ................................................................. 1
1.1. Background ................................................................................................... 1
1.2. Motivation .................................................................................................... 2
1.3. Thesis Organization ...................................................................................... 4
CHAPTER 2 – Literature Review ....................................................... 5
2.1. Recommender Systems ................................................................................ 5
2.1.1. Collaborative Filtering Methods ....................................................... 6
2.1.2. Content-based Recommendation Methods ....................................... 8
2.2. Partially supervised classification ............................................................... 11
2.3. Travel threads classification ....................................................................... 14
2.4. Q&A Recommendation .............................................................................. 17
CHAPTER 3 – The Model and Methods ........................................... 20
3.1. Perspective descriptions ............................................................................. 20
3.1.1. Goal similarity ................................................................................ 20
3.1.2. Content Similarity .......................................................................... 22
3.1.3. Freshness ........................................................................................ 23
3.1.4. Quality ............................................................................................ 24
3.2. Recommending Threads ............................................................................. 26
3.2.1. Question-based method .................................................................. 27
3.2.2. Session-based method .................................................................... 29
3.2.3. Hybrid method ................................................................................ 29
CHAPTER 4 – Evaluation .................................................................. 30
4.1. Data Collection ........................................................................................... 30
4.2. Experiment Design ..................................................................................... 31
4.3. Performance Results ................................................................................... 33
4.3.1. The MAE of the three methods ...................................................... 34
4.3.2. The impact of session size on MAE ............................................... 35
4.3.3. The impact of goal .......................................................................... 36
CHAPTER 5 – Conclusions ................................................................ 39
5.1. Future work ................................................................................................ 40
References ................................................................................................ 41
參考文獻 References
References
Adamic, L. A., Zhang, J., Bakshy, E., & Ackerman, M. S. (2008). Knowledge sharing
and yahoo answers: everyone knows something. Proceedings of the 17th
international conference on World Wide Web, 665-674.
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender
systems: A survey of the state-of-the-art and possible extensions. IEEE
Transactions Knowledge and Data Engineering, 17. (6)
Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding
high-quality content in social media. Proceedings of the international conference
on Web search and web data mining, 183-194.
Ardissono, L., Goy, A., Petrone, G., Segnan, M., & Torasso, P. (2003). Intrigue:
personalized recommendation of tourist attractions for desktop and hand held
devices. Applied Artificial Intelligence, 17(8-9), 687-714.
Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative
recommendation. Communications of the ACM, 40(3), 66-72.
Berger, A., Caruana, R., Cohn, D., Freitag, D., & Mittal, V. (2000). Bridging the lexical
chasm: statistical approaches to answer-finding. Proceedings of the 23rd Annual
Conference on Research and Development in Information Retrieval, 192-199.
Burke, R. D., Hammond, K. J., Kulyukin, V., Lytinen, S. L., Tomuro, N., & Schoenberg,
S. (1997). Question answering from frequently asked question files: Experiences
with the faq finder system. AI magazine, 18(2), 57.
Delgado, J., & Davidson, R. (2002). Knowledge bases and user profiling in travel and
hospitality recommender systems. Proceedings of the ENTER 2002 Conference,
1-16.
Ding, Y., & Li, X. (2005). Time weight collaborative filtering. Proceedings of the 14th
ACM international conference on Information and knowledge management,
485-492.
Felfernig, A., Gordea, S.,Jannach,D.,Teppan,E., & Zanker,M. (2006). A Short Survey of
Recommendation Technologies in Travel and Tourism. OGAI.
Grčar, M., Mladenič, D., Fortuna, B., & Grobelnik, M. (2006). Data sparsity issues in
the collaborative filtering framework. Advances in Web Mining and Web Usage
Analysis, 58-76.
Gretzel, U., & Yoo, K. H. (2008). Use and impact of online travel reviews. Information
and communication technologies in tourism 2008, 35-46.
Gretzel, U., Yoo, K. H., & Purifoy, M. (2007). Online travel review study: Role and
impact of online travel reviews. Laboratory for Intelligent Systems in Tourism,
College Station.
Jeon, J., Croft, W. B., & Lee, J. H. (2005a). Finding semantically similar
questions based on their answers. Proceedings of the 28th annual international ACM
SIGIR conference on Research and development in information retrieval,
617-618.
Jeon, J., Croft, W. B., & Lee, J. H. (2005b). Finding similar questions in large question
and answer archives. Proceedings of the 14th ACM international conference on
Information and knowledge management, 84-90.
Li, X. L., Liu, B., & Ng, S. K. (2010). Negative training data can be harmful to text
classification. Empirical Methods in Nature Language Processing.
Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item
collaborative filtering. IEEE Internet Computing, 7(1), 76-80.
Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2003). Building text classifiers using
positive and unlabeled examples. Proceedings of the Third IEEE International
Conference on Data Mining, 179-186.
Liu, B., Lee, W. S., Yu, P. S., & Li, X. (2002). Partially supervised classification of text
documents. Proceedings of the Nineteenth International Conference on Machine
Learning.
O'Mahony, M. P., & Smyth, B. (2009). Learning to recommend helpful hotel reviews.
Proceedings of the third ACM conference on Recommender systems.
O’Connor, P. (2008). User-generated content and travel: A case study on
Tripadvisor. com. Information and communication technologies in tourism 2008, 47-58.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: An
open architecture for collaborative filtering of netnews. Proceedings of the 1994
ACM conference on Computer supported cooperative work, 175-186.
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the
ACM, 40(3), 56-58.
Saga, R., Hayashi, Y., & Tsuji, H. (2008). Hotel recommender system based on user's
preference transition. IEEE International Conference on Systems, Man &
Cybernetics, 2437-2442.
Schafer, J., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering
recommender systems. The adaptive web, 291-324.
Schafer, J. B., Konstan, J., & Riedi, J. (1999). Recommender systems in e-commerce.
Proceedings of the 1st ACM conference on Electronic commerce, 158-166.
Sheldon, P. J. (1997). Tourism information technology. Cab International New York.
Si, L., & Jin, R. (2003). Flexible mixture model for collaborative filtering. Proc. 20th
international conference on Machine Learning, 20, 704.
Si, X., Chang, E. Y., Gyongyi, Z., & Sun, M. (2010). Confucius and its intelligent
disciples: integrating social with search. Proceedings of the VLDB Endowment.
Suryanto, M. A., Lim, E. P., Sun, A., & Chiang, R. H. L. (2009). Quality-aware
collaborative question answering: methods and evaluation. Proceedings of the
Second ACM International Conference on Web Search and Data Mining,
142-151.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code