根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：9

、訪客IP：18.191.233.122

姓名

陳昭宇(Chao-Yu Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統
(A SOMO-based Recommendation System)

相關論文

★ 以Q-學習法為基礎之群體智慧演算法及其應用	★ 發展遲緩兒童之復健系統研製
★ 從認知風格角度比較教師評量與同儕互評之差異：從英語寫作到遊戲製作	★ 基於檢驗數值的糖尿病腎病變預測模型
★ 模糊類神經網路為架構之遙測影像分類器設計	★ 複合式群聚演算法
★ 身心障礙者輔具之研製	★ 指紋分類器之研究
★ 背光影像補償及色彩減量之研究	★ 類神經網路於營利事業所得稅選案之應用
★ 一個新的線上學習系統及其於稅務選案上之應用	★ 人眼追蹤系統及其於人機介面之應用
★ 結合群體智慧與自我組織映射圖的資料視覺化研究	★ 追瞳系統之研發於身障者之人機介面應用
★ 以類免疫系統為基礎之線上學習類神經模糊系統及其應用	★ 基因演算法於語音聲紋解攪拌之應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限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。

摘要(中)

由於網頁資訊藉著分散式架構的網路環境快速擴散，造成網路上的資訊越來越龐大。在這樣的環境下，使用者取得最適當的資訊也不會是件容易的事，因此為了協助人們快速找尋恰當的資訊，資訊過濾技術的引用便快速地發展起來，而推薦系統便是資訊過濾裡頭的一種智慧型網路技術。推薦系統推薦使用者喜歡的物品，去除使用者不喜歡的資訊。本篇論文提出以自我組織特徵映射圖之優化演算法(Self-Organizing Feature Map-based Optimization，簡稱SOMO)為基礎的推薦系統，來預測使用的喜好並給予適當的建議及推薦，並且將此系統應用於電影推薦上。除了將找推薦者的動作變成兩階段分群外，還改良協同式過濾(Collaborative Filtering)推薦系統在“冷啟動”(cold start)時無法找到推薦者的問題。經由兩階段的分群，日後進入系統的使用者及物品可以快速分配到自己所屬的群落。
在本篇論文中，旅遊者推薦系統是推薦系統的另一個應用，而旅遊者推薦系統被包含在進階旅遊者資訊系統(Advanced Travel Information Systems，簡稱ATIS)的領域。為了能有效達成旅遊者希望完成的旅程，進階旅遊者資訊系統希望提供旅遊者旅遊的相關資訊。在本篇推薦系統是屬於進階資訊系統裡的預先規畫的行程(pre-trip)部分，我們首先收集了有關景點、旅程以及使用者對旅程評分的資料庫，再套用上述之推薦系統，便可預測使用者喜好進行旅程推薦，旅程選擇還可以根據景點的經緯度來決定。為了將推薦系統做成能在車內即時使用，考慮了頻寬的限制，我們還對路徑資訊傳輸做了優化的研究。

摘要(英)

In a large-scale distributed network environment like internet, information has been increased and changed continuously. Accessing information in such dynamically changing, heterogeneous and world-wide distributed environments puts a big burden on the users. A possible solution to alleviate information overload by identifying which information a user will find worthwhile is the use of recommendation systems. Recommendation Systems are a kind of web intelligence techniques to make daily information filtering for people. In this paper, we propose a SOMO-based recommendation system which is able to learn personal preferences of users and provide tailored suggestions to users.
The goal of Advanced Traveler Information Systems (ATIS) is to provide travelers with the information required to efficiently and effectively complete a desired trip. We construct a web site to collect route databases and use the SOMO-based recommendation system to implement a tourist spots recommendation system. Tourist spots that meet the travelers’ preferences will be suggested by the proposed recommendation system. In addition, we also proposed an algorithm to alleviate the heavy load for transmitting the rout maps. Several experiments were conducted to test the performance of the proposed recommendation system.

論文目次

第一章緒論 1
1.1 推薦系統 1
1.2 優化演算法 2
1.3 研究動機 3
1.4 論文架構 5
第二章文獻回顧 7
2.1 推薦系統種類 7
2.1.1 協同式推薦系統(Collaborative filtering) 7
2.1.2 內容式推薦系統(Content-based filtering) 11
2.1.3 混合式推薦系統(Hybrid approach) 12
2.2 優化演算法種類 14
2.2.1 粒子族群優化 14
2.2.2 基因演算法 15
2.2.3 自我組織特徵映射圖優化 17
第三章研究方法與步驟 22
3.1 以SOMO為基礎的協同式過濾推薦系統 23
3.1.1 產品分群 24
3.1.2 使用者分群 25
3.1.3 預測模組 26
3.1.4 優化相似度 27
3.1.5 個人化推薦 29
3.2 內容式推薦系統 29
3.2.1 特徵點擷取 30
3.2.2 線性預測模組 31
3.2.3 最佳解調整機制 31
3.2.4 個人化推薦 32
3.3 結合協同式及內容式推薦系統 32
3.4 冷啟動問題 34
第四章模擬結果與比較 37
第五章應用於進階旅遊者資訊系統的推薦系統 44
5.1 進階旅遊者資訊系統 44
5.2 應用在進階旅遊者資訊系統的推薦系統 45
5.2.1 資料庫的建置 46
5.2.2 以SOMO為基礎的推薦系統 47
5.2.3 個人化旅程推薦 48
5.2.4 模擬結果 49
第六章結論與展望 51
6.1 結論 51
6.2 未來研究方向 52
參考文獻 54
附錄A 路徑減量 58
A.1 研究動機 58
A.2 目標函數設計 59
A.3 啟發式減量演算法 60
A.4 優化演算法的應用 60
A.5 模擬結果 62

參考文獻

[1] MovieLens, http://www.movielens.umn.edu
[2] Naruwan, http://www.tbroc.gov.tw/lan/cht/index/
[3] The Internet Movie Database (IMDb), http://www.imdb.com
[4] J. A. Alspector, A. Kolcz, and N. Karunanithi, “Comparing feature-based and clique-based user models for movie selection,” in Proc. of the Third ACM Conference on Digital Libraries, 1998.
[5] M. Balabanovic and Y. Shoham, “Fab: Content-based, collaborative recommendation,” Communications of the ACM, Vol. 40, No. 3, pp. 66-72, 1997.
[6] D. Billsus and M. J. Pazzani, “Learning Collaborative Information Filters,” in Proceedings of the 15th International Conference on Machine Learning, pp. 46-54.
[7] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” in Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), pp.43-52, 1998.
[8] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. netes, and M. Sartin, “Combining content-based and collaborative filters in an online newspaper,” in Proc. ACM-SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, 1999.
[9] J. Delgado, N. Ishii, and T. Ura., “Content-based collaborative information filtering: Actively learning to classify and recommend documents,” in Proc. Second Int. Workshop, CIA’98, pp. 206-215, 1998.
[10] R. C. Eberhart and Y. Shi, “Particle Swarm Optimization: Developments, Applications and Resuources,” in Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 1, pp. 81-86, 2001
[11] D. E. Goldberg, “Genetic Algorithms in Search,” Optimization, and Machine Learning, Addison-Wesley Publishing Company, Reading, MA, 1989
[12] N. Kase, M. Hattri, A. Ohsuga and S. Honiden, “InfoMirror-Agent-based Information Assistance to Drivers,” in IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp. 734-739, 1999.
[13] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
[14] A. Kirson, “ATIS- a modular approach,” IEEE PLANS in Position Location and Navigation Symposium, pp. 528-533, 1992.
[15] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical statistics and probability, pp. 281-297, 1967.
[16] N. Kohata, T. Yamaguchi, M. Sato, T. Baba, and H. Hashimoto, “Dynamic Formation Generating for Intelligent Transport Systems using Algorithm to Select Function by Environment Information.,” in IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp. 798-803, 1999.
[17] T. Kohonen, “Self-Organization Maps,” Springer-Verlag, 1995.
[18] V. Kostov, E. Naito, and J. Ozawa, “Cellular Phone Ringing Tone Recommendation System Based on Collaborative Filtering Method,” in Proc. of IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 378-383, 2003.
[19] Q. Li and B. M. Kim, “Clustering Approach for Hybrid Recommender System,” in Proc. of the IEEE/WIC International Conference on Web Intelligence, pp.33-38, 2003.
[20] R. Liu, D. V. Vliet, and D. Watling, “DRACULA: Dynamic Route Assignment Combining User Learning and micro-simulation,” in Proceedings of the 23rd European Transport Forum. PTRC, Vol. E, pp. 143-152, 1995.
[21] P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl, “Grouplens: An open architecture for collaborative filtering of netnews,” in Proc. ACM Conf. On Computing Systems, pp. 210-217, 1995.
[22] R. J. F. Rossetti, S. Bampi, R. Liu, and D.V. Vliet, “An Agent-based Framework for the Assessment of Drivers’ Decision-Making,” in Proceedings of 2000 IEEE International Conference on Intelligent Transportation Systems, pp. 387-392, 2000.
[23] G. Salton and MJ McGill, “Introduction to Modern Information Retrieval,” McGraw-Hill, New York, 1983
[24] B.Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of Recommendation Algorithms for E-Commerce,” in Proceedings of the ACM E-Commerce 2000 Conference, pp. 158-167, 2000.
[25] U. Shardanand, and P. Maes, “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’,” in Proceedings of the Conference on Human Factors in Computing Systems (CHI95), pp. 210-217, 1995.
[26] M. C. Su, Y. X. Zhao, and J. Lee, “SOM-based optimization,” in IEEE International Joint Conference on Neural Networks IJCNN, Vol. 1, pp. 25-29, 2004.
[27] C. Thorpe, T. Jochem, and D. Pomerleau, “The 1997 Automated Highway Free Agent Demonstration,” in IEEE Conference on Intelligent Transportation Systems, pp. 496-501, 1997.
[28] S. Ujjin and P. J. Bentley, “Particle swarm optimization recommender system,” in Proc. of the IEEE Swarm Intelligence Symposium, pp. 124-131, 2003
[29] C. C. White, III, “Intelligent transportation systems: integrating information technology and the surface transportation system,” in IEEE International Conference on Systems, Man and Cybernetics, Vol. 5, pp.4000-4003, 1995.
[30] T. Yamaguchi, “Fuzzy Associative Memory Organizing Units System,” Journal of Japan Society for Fuzzy Theory and Systems, Vol. 5, No. 2, pp. 245-260, 1993.

指導教授

蘇木春(Mu-Chun Su)

審核日期

2005-7-15

推文