Publications-Theses
Article View/Open
Publication Export
-
題名 運用跨城市關係推薦個人化旅遊景點
Personalized Tourist Attractions Recommendation using Cross-City Relationship作者 曾筱筑
Zeng, Siao-Jhu貢獻者 沈錳坤
Shan, Man-Kwan
曾筱筑
Zeng, Siao-Jhu關鍵詞 景點推薦
主題模型
相關性探勘
Attractions recommendation
Topic modeling
Correlation mining日期 2018 上傳時間 3-Sep-2018 15:52:18 (UTC+8) 摘要 隨著全球化的發展以及社群媒體的流行，自助旅行的風氣蔚為風潮。由於現在網路上有大量的旅遊資訊，當使用者想規劃一個目的地旅遊時，使用者輸入目的地名稱則會顯示出許多筆資料。然而接收過多的旅遊資訊反而會讓使用者更感困惑，不知如何著手規劃旅遊行程，因此也開始有自動推薦旅遊景點的需求。與傳統推薦方法相比，旅遊景點推薦需克服沒有實際使用者對旅遊景點的評分，以及使用者拜訪旅遊景點次數少，造成資料稀疏性的問題。在過去旅遊景點推薦的相關研究中，大多只考慮使用者之間潛在的喜好相似度，來改善協同過濾方法中的資料稀疏性問題，較少考慮到旅遊景點潛在的影響力以及不同城市旅遊景點的差異。本研究期望從大量的地理標籤照片中得到使用者旅遊紀錄，透過Latent Dirichlet Allocation (LDA)學習城市中旅遊景點有價值的潛在資訊與使用者對該城市潛在的喜好資訊，再透過Partial Least Square Regression (PLSR)將不同的城市視為不同的領域，找到跨城市之間的關係，為使用者個人化推薦符合該使用者喜好的旅遊景點。本研究的目標為，當使用者要拜訪目標城市的旅遊景點時，運用考量旅遊次數的使用者拜訪旅遊景點的情形與跨城市之間的關係，推薦符合使用者喜好的目標城市旅遊景點。經過實驗評估，證實本研究與傳統推薦方法相比，能有效提升個人旅遊景點推薦的效能及準確度。
With the development of globalization and the popularity of social media, self-guided tour has become a trend. It takes much time for a user to plan a tour, including searching for attractions. Therefore, the demand for automatic attractions recommendation for self-guided tourist is rising.Compared with the traditional recommendation mechanism, tourist attractions recommendation needs to overcome no user rating scores for tourist attractions and data sparsity, because most users usually visit only few attractions. In the previous research, most work only considers the similarity between users to improve the data sparsity problem based on collaborative filtering. Less consideration is paid to the correlation of visited attractions between tourist destinations. In this thesis, we collected user travel records from a large number of geo-tagged photos, and utilized Latent Dirichlet Allocation (LDA) to discover the preference distribution of each tourist for each destination. Then, Partial Least Square Regression (PLSR) is employed to find the correlation relationship of preference distributions between tourist destinations. The attractions are personally recommended based on the user’s preferences and the discovered correlation relationships. The experiment shows our proposed method is better than other approaches.參考文獻 [1] S. Amer-Yahia, S. B. Roy, A. Chawlat, G. Das, and C. Yu, Group recommendation: Semantics and efficiency. Proceedings of the VLDB Endowment, 2(1), 2009.[2] D. M. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.[3] D. M. Blei, Probabilistic topic models, Communications of the ACM, 55(4), 77-84, 2012.[4] I. Cantador, I. Fernandez-Tobas, S. Berkovsky, and P. Cremonesi, Cross-domain recommender systems. Recommender Systems Handbook (2015), 919–959, 2015.[5] A. Cheng, Y. Chen, Y. Huang, W. Hsu, and H. Liao, Personalized travel recommendation by mining people attributes from communitycontributed photos, in Proceedings of 19th ACM international conference on Multimedia, 83–92, 2011.[6] D. Comaniciu, P. Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.[7] A. M. Elkahky, Y. Song, and X. He, A multi-view deep learning approach for cross domain user modeling in recommendation systems. Proceedings of the 24th International Conference on World Wide Web, 2015.[8] M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. Knowledge Discovery in Databases, 1996.[9] A. Farseev, I. Samborskii, A. Filchenkov, and T.-S. Chua, Cross-Domain Recommendation via Clustering on Multi-Layer Graphs. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’17), 195–204, 2017.[10] Y. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa, Fast and exact top-k search for random walk with restart. Proceedings of the VLDB Endowment, 5(5), 2012.[11] L. Guo, J. Shao, K. L. Tan, and Y. Yang, Wheretogo: Personalized travel recommendation for individuals and groups. Proceedings of the 15th IEEE International Conference on Mobile Data Management (MDM), 2014.[12] S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Transactions on Multimedia, 17(6), 2015.[13] S. Kisilevich, F. Mansmann, and D. Keim, P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. Proceedings of the 1st international conference and exhibition on computing for geospatial research & application, 2010.[14] H. Kori, S. Hattori, T. Tezuka, and K. Tanaka, Automatic generation of multimedia tour guide from local blogs. Proceedings of the 13th international conference on Multimedia Modeling, 2007.[15] C. Li, C. Hsu, and M. Shan. A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression. ACM Transactions on Intelligent Systems and Technology (TIST), 2018. (to appear)[16] J. Lian, F. Zhang, X. Xie, and G. Sun, CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. Proceedings of the 26th International Conference on World Wide Web Companion, 2017.[17] G. Linden, B. Smith, and J. York, Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 2003.[18] J. Liu, Z. Huang, L. Chen, H. T. Shen, and Z. Yan, Discovering areas of interest with geo-tagged images and check-ins. Proceedings of the 20th ACM international conference on Multimedia, 2012.[19] Q. Liu, Y. Ge, Z. Li, E. Chen, and H. Xiong, Personalized travel package recommendation. Proceedings of the 11th IEEE International Conference on Data Mining (ICDM), 2011.[20] R. Manne, Analysis of two partial-least-squares algorithms for multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 2(1), 187-197, 1987.[21] S.Qian, T. Zhang, R. Hong, and C. Xu, Cross-Domain Collaborative Learning in Social Multimedia. In Proceedings of the 23rd ACM International Conference on Multimedia (MM ’15), 99–108, 2015.[22] M. Röder , A. Both , A. Hinneburg, Exploring the Space of Topic Coherence Measures. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 2015.[23] R. Rosipal and N. Krämer, Overview and recent advances in partial least squares. Subspace, Latent Structure and Feature Selection, Springer, 34-51, 2006[24] B. Thomee, D. A. Shamma,G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and Li-Jia Li, Yfcc100m: The new data in multimedia research. Communications of the ACM, volume59, pages 64–73, 2016.[25] H. Tong, C. Faloutsos, and J. Y. Pan, Fast random walk with restart and its applications. Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), 2006.[26] L. Y. Wei, Y. Zheng, and W. C. Peng, Constructing popular routes from uncertain trajectories. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012.[27] H. Wold, H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, V. Capecchi, Path models with latent variables: The NIPALS approach. in Quantitative Sociology: Internnational Perspectives on Mathematical and Statistical Model Building, pp. 307-357, 1975.[28] Z. Xu, L. Chen, Y. Dai, and G. Chen, A dynamic topic model and matrix factorization based travel recommendation method exploiting ubiquitous data. IEEE Transactions on Multimedia, 19(8), 2017.[29] 陳逸群, 旅遊行程自動規劃系統的設計與實作. 國立政治大學資訊科學系, 碩士論文, 2016. 描述 碩士
國立政治大學
資訊科學系
105753010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105753010 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man-Kwan en_US dc.contributor.author (Authors) 曾筱筑 zh_TW dc.contributor.author (Authors) Zeng, Siao-Jhu en_US dc.creator (作者) 曾筱筑 zh_TW dc.creator (作者) Zeng, Siao-Jhu en_US dc.date (日期) 2018 en_US dc.date.accessioned 3-Sep-2018 15:52:18 (UTC+8) - dc.date.available 3-Sep-2018 15:52:18 (UTC+8) - dc.date.issued (上傳時間) 3-Sep-2018 15:52:18 (UTC+8) - dc.identifier (Other Identifiers) G0105753010 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119911 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 105753010 zh_TW dc.description.abstract (摘要) 隨著全球化的發展以及社群媒體的流行，自助旅行的風氣蔚為風潮。由於現在網路上有大量的旅遊資訊，當使用者想規劃一個目的地旅遊時，使用者輸入目的地名稱則會顯示出許多筆資料。然而接收過多的旅遊資訊反而會讓使用者更感困惑，不知如何著手規劃旅遊行程，因此也開始有自動推薦旅遊景點的需求。與傳統推薦方法相比，旅遊景點推薦需克服沒有實際使用者對旅遊景點的評分，以及使用者拜訪旅遊景點次數少，造成資料稀疏性的問題。在過去旅遊景點推薦的相關研究中，大多只考慮使用者之間潛在的喜好相似度，來改善協同過濾方法中的資料稀疏性問題，較少考慮到旅遊景點潛在的影響力以及不同城市旅遊景點的差異。本研究期望從大量的地理標籤照片中得到使用者旅遊紀錄，透過Latent Dirichlet Allocation (LDA)學習城市中旅遊景點有價值的潛在資訊與使用者對該城市潛在的喜好資訊，再透過Partial Least Square Regression (PLSR)將不同的城市視為不同的領域，找到跨城市之間的關係，為使用者個人化推薦符合該使用者喜好的旅遊景點。本研究的目標為，當使用者要拜訪目標城市的旅遊景點時，運用考量旅遊次數的使用者拜訪旅遊景點的情形與跨城市之間的關係，推薦符合使用者喜好的目標城市旅遊景點。經過實驗評估，證實本研究與傳統推薦方法相比，能有效提升個人旅遊景點推薦的效能及準確度。 zh_TW dc.description.abstract (摘要) With the development of globalization and the popularity of social media, self-guided tour has become a trend. It takes much time for a user to plan a tour, including searching for attractions. Therefore, the demand for automatic attractions recommendation for self-guided tourist is rising.Compared with the traditional recommendation mechanism, tourist attractions recommendation needs to overcome no user rating scores for tourist attractions and data sparsity, because most users usually visit only few attractions. In the previous research, most work only considers the similarity between users to improve the data sparsity problem based on collaborative filtering. Less consideration is paid to the correlation of visited attractions between tourist destinations. In this thesis, we collected user travel records from a large number of geo-tagged photos, and utilized Latent Dirichlet Allocation (LDA) to discover the preference distribution of each tourist for each destination. Then, Partial Least Square Regression (PLSR) is employed to find the correlation relationship of preference distributions between tourist destinations. The attractions are personally recommended based on the user’s preferences and the discovered correlation relationships. The experiment shows our proposed method is better than other approaches. en_US dc.description.tableofcontents 第一章 緒論 11.1 研究背景 11.2 研究動機與目的 3第二章 相關研究 52.1 旅遊推薦的資料類型 52.2 個人化旅遊景點推薦方法 62.3 跨領域推薦方法 8第三章 研究方法 103.1 推薦目標與推薦情境之定義 103.2 研究架構 123.3 旅遊相關資料蒐集 143.3.1 Flickr地理標籤相片 143.3.2 YFCC100M資料集 163.4 使用者旅遊紀錄探勘 183.4.1 旅遊景點取得 183.4.2 使用者旅遊紀錄取得 193.5 個人化旅遊景點推薦 203.5.1 城市旅遊喜好與城市旅遊類型探勘 203.5.2 跨城市關係學習 233.5.3 推薦目標城市之旅遊景點 26第四章 實驗設計與結果分析 284.1 資料概況 284.2 實驗設計 294.3 評估方法 314.4 比較推薦方法 324.5 實驗評估結果 334.6 實驗結果分析 354.6.1 各城市旅遊類型主題數量選擇 354.6.2 城市關係度高之城市實驗組合 364.6.3 城市關係度低之城市實驗組合 384.6.4 地域性影響之分析 394.6.5 城市主題範例 424.6.6 跨城市關係範例 434.6.7 查詢使用者推薦旅遊景點範例 45第五章 結論與未來研究 47參考文獻 48 zh_TW dc.format.extent 2239974 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105753010 en_US dc.subject (關鍵詞) 景點推薦 zh_TW dc.subject (關鍵詞) 主題模型 zh_TW dc.subject (關鍵詞) 相關性探勘 zh_TW dc.subject (關鍵詞) Attractions recommendation en_US dc.subject (關鍵詞) Topic modeling en_US dc.subject (關鍵詞) Correlation mining en_US dc.title (題名) 運用跨城市關係推薦個人化旅遊景點 zh_TW dc.title (題名) Personalized Tourist Attractions Recommendation using Cross-City Relationship en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] S. Amer-Yahia, S. B. Roy, A. Chawlat, G. Das, and C. Yu, Group recommendation: Semantics and efficiency. Proceedings of the VLDB Endowment, 2(1), 2009.[2] D. M. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.[3] D. M. Blei, Probabilistic topic models, Communications of the ACM, 55(4), 77-84, 2012.[4] I. Cantador, I. Fernandez-Tobas, S. Berkovsky, and P. Cremonesi, Cross-domain recommender systems. Recommender Systems Handbook (2015), 919–959, 2015.[5] A. Cheng, Y. Chen, Y. Huang, W. Hsu, and H. Liao, Personalized travel recommendation by mining people attributes from communitycontributed photos, in Proceedings of 19th ACM international conference on Multimedia, 83–92, 2011.[6] D. Comaniciu, P. Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.[7] A. M. Elkahky, Y. Song, and X. He, A multi-view deep learning approach for cross domain user modeling in recommendation systems. Proceedings of the 24th International Conference on World Wide Web, 2015.[8] M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. Knowledge Discovery in Databases, 1996.[9] A. Farseev, I. Samborskii, A. Filchenkov, and T.-S. Chua, Cross-Domain Recommendation via Clustering on Multi-Layer Graphs. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’17), 195–204, 2017.[10] Y. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa, Fast and exact top-k search for random walk with restart. Proceedings of the VLDB Endowment, 5(5), 2012.[11] L. Guo, J. Shao, K. L. Tan, and Y. Yang, Wheretogo: Personalized travel recommendation for individuals and groups. Proceedings of the 15th IEEE International Conference on Mobile Data Management (MDM), 2014.[12] S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Transactions on Multimedia, 17(6), 2015.[13] S. Kisilevich, F. Mansmann, and D. Keim, P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. Proceedings of the 1st international conference and exhibition on computing for geospatial research & application, 2010.[14] H. Kori, S. Hattori, T. Tezuka, and K. Tanaka, Automatic generation of multimedia tour guide from local blogs. Proceedings of the 13th international conference on Multimedia Modeling, 2007.[15] C. Li, C. Hsu, and M. Shan. A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression. ACM Transactions on Intelligent Systems and Technology (TIST), 2018. (to appear)[16] J. Lian, F. Zhang, X. Xie, and G. Sun, CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. Proceedings of the 26th International Conference on World Wide Web Companion, 2017.[17] G. Linden, B. Smith, and J. York, Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 2003.[18] J. Liu, Z. Huang, L. Chen, H. T. Shen, and Z. Yan, Discovering areas of interest with geo-tagged images and check-ins. Proceedings of the 20th ACM international conference on Multimedia, 2012.[19] Q. Liu, Y. Ge, Z. Li, E. Chen, and H. Xiong, Personalized travel package recommendation. Proceedings of the 11th IEEE International Conference on Data Mining (ICDM), 2011.[20] R. Manne, Analysis of two partial-least-squares algorithms for multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 2(1), 187-197, 1987.[21] S.Qian, T. Zhang, R. Hong, and C. Xu, Cross-Domain Collaborative Learning in Social Multimedia. In Proceedings of the 23rd ACM International Conference on Multimedia (MM ’15), 99–108, 2015.[22] M. Röder , A. Both , A. Hinneburg, Exploring the Space of Topic Coherence Measures. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 2015.[23] R. Rosipal and N. Krämer, Overview and recent advances in partial least squares. Subspace, Latent Structure and Feature Selection, Springer, 34-51, 2006[24] B. Thomee, D. A. Shamma,G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and Li-Jia Li, Yfcc100m: The new data in multimedia research. Communications of the ACM, volume59, pages 64–73, 2016.[25] H. Tong, C. Faloutsos, and J. Y. Pan, Fast random walk with restart and its applications. Proceedings of the 6th IEEE International Conference on Data Mining (ICDM), 2006.[26] L. Y. Wei, Y. Zheng, and W. C. Peng, Constructing popular routes from uncertain trajectories. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012.[27] H. Wold, H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, V. Capecchi, Path models with latent variables: The NIPALS approach. in Quantitative Sociology: Internnational Perspectives on Mathematical and Statistical Model Building, pp. 307-357, 1975.[28] Z. Xu, L. Chen, Y. Dai, and G. Chen, A dynamic topic model and matrix factorization based travel recommendation method exploiting ubiquitous data. IEEE Transactions on Multimedia, 19(8), 2017.[29] 陳逸群, 旅遊行程自動規劃系統的設計與實作. 國立政治大學資訊科學系, 碩士論文, 2016. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.CS.011.2018.B02 -
