推薦系統能協助人們從氾濫的資料中粹取人們所需的，但要能精準的推薦仍是相當困難的工程。隨著行動通訊的快速發展，已經有越來越多應用在手持裝置的推薦服務。而我們主要著重在行動環境下，利用非同布分享進行旅遊景點的推薦。當遊客們造訪一個新的景點，他可以與當地的景點系統做資料的分換，使遊客能得到其他擁有相同興趣的人對於其它拜訪過景點的評分資料，整合使用者與其它人的評分表以做為使用者在決定下一個景點的參考。
我們提出了四種交換方法，並假設景點系統一開始擁有不同數量的歷史資料，分別去比較各種結果。實驗結果表現了當景點系統擁有足夠的資料時，都能達到極佳的推薦效果。此外，當景點系統存在相同的資料時，我們也評估各方法的優點及缺點。最後，我們會比較同步和非同步的環境，在景點交換時的效果。

Despite recommender systems being useful, for some applications it is hard to accumulate all the required information needed for the recommendation. In today‟s ubiquitous environment, mobile devices with different characteristics are widely available. Our work focuses on the recommendation service built on mobile environment to support tourists‟ traveling need. When tourists visit a new attraction, their recommender systems can exchange data with the attraction system to help obtain rating information of people with similar tastes. Such asynchronous rating exchange mechanisms allow a tourist to receive ratings from other people even though they may not collocate at the same time.
We proposed four data exchange methods between a user and an attraction system. Our recommendation mechanism incorporates other users‟ opinions to provide recommendations once the user has collected enough ratings. Every method is compared under four conditions which attraction systems carry different amount of existing data. Then we compare these methods under different amount of existing rating data and shed the light on their advantages and disadvantages. Finally, we compare our proposed asynchronous methods with other synchronous data exchange methods proposed previously.

Chapter 1 – Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Thesis Organization 2
Chapter 2 – Literature Review 3
2.1 Recommender Systems 3
2.1.1 Content-based Recommendation Method 4
2.1.2 Collaborative Filtering Recommendation Method 6
2.2 Travel Recommender Systems 7
2.3 Mobile Recommendation 9
2.4 Distributed Recommendation 10
Chapter 3 - Data Exchange Methods 12
3.1 The Architecture 12
3.2 The Methods 13
3.3 Recommendation Mechanisms 21
Chapter 4 – Preliminary Experiments and Results 23
4.1 Simulation Model 23
4.2 Simulation Environment 25
4.3 Preliminary Experiments for Different Values of D in Hybrid Data Exchange Method 28
4.3.1 Experimental Results 28
4.4 Preliminary Experiments for Different Values of N in Aggregate Data Exchange Method 31
4.4.1 Experimental Results 31
Chapter 5 –Simulation Results and Evaluations 35
5.1 Performance Metrics 35
5.2 Four Methods with Different Amount of History Data 36
5.2.1 Results and Evaluations of the Self Preference-based Method 37
5.2.2 Results and Evaluations of the Collected Preference-based Method 40
5.2.3 Results and Evaluations of the Hybrid Method 43
5.2.4 Results and Evaluations of the Aggregate Method 45
5.3 Four Methods in the Same Rounds of Data 48
5.3.1 Results and Evaluations of the Cold Start 48
5.3.2 Results and Evaluations of the One Round of Data 52
5.3.3 Results and Evaluations of the Two Rounds of Data 54
5.4 Comparing synchronous and asynchronous data exchange methods 57
5.4.1 Results and Evaluations of the Preference-based Method in Different Environment 57
5.4.2 Results and Evaluations of the Hybrid Method in Different Environment 60
Chapter 6 – Conclusions 62
Reference 64

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 on Knowledge and Data Engineering, 17(6), 734-749.
Alspector, J., Kolcz, A., & Karunanithi, N. (1998). Comparing feature-based and clique-based user models for movie selection. Proceedings of the Third ACM Conference on Digital libraries, 11-18.
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), 687-714.
Balabanovi , M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 72.
Breese, J., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 43-52.
De Spindler, R., Norrie, M. C., Grossniklaus, M., & Signer, B. (2006). Spatio-temporal proximity as a basis for collaborative filtering in mobile environments. Proceedings of the CAISE*06 Workshop on Ubiquitous Mobile Information and Collaboration Systems, Luxemburg.
Fong, R., & Si, Y. Design of a Recommender System for Mobile Tourism Multimedia Selection. 2nd International Conference on Internet Multimedia Services Architecture and Application, 1-6.
Goldberg, D., Nichols, D., Oki, B., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 70.
Herlocker, J., Konstan, J., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 230-237.
Jacobsson, M., Rost, M., & Holmquist, L. (2006). When Media Gets Wise: collaborative filtering with mobile media agents. Proceedings of the 11th International Conference on Intelligent User Interfaces, 291-293.
Krosche, J., Baldzer, J., & Boll, S. (2004). Mobidenk-mobile multimedia in monument conservation. IEEE Multimedia, 11(2), 72-77.
Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76-80.
Liu, C., Sun, C., & Fang, M. (2008). The design of an open hybrid recommender system for mobile commerce. Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation.
Nguyen, Q., Cavada, D., & Ricci, F. (2003). Trip@ dvice Mobile Extension of a Casebased Travel Recommender System. Proceedings of the 2nd International Conference on Mobile Business, 345-356.
Nützel, J., & Kubek, M. (2006). A mobile peer-to-peer application for distributed recommendation and re-sale of music. Automated Production of Cross Media Content for Multi-Channel Distribution, 2006. AXMEDIS'06, 93-98.
O'Donovan, J., & Smyth, B. (2005). Trust in recommender systems. Proceedings of the 10th International Conference on Intelligent User Interfaces, 167-174.
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. (1997). Recommender systems. Communications of the ACM, 40(3), 58.
Schafer, J. B., Konstan, J., & Riedi, J. (1999). Recommender systems in e-commerce. Proceedings of the 1st ACM Conference on Electronic Commerce, 158-166.
Sebastia, L., Garcia, I., Onaindia, E., & Guzman, C. (2008). e-Tourism: a tourist recommendation and planning application. IEEE International Conference on Tools with Artificial Intelligence (ICTAL 2008), 89-96.
Van Setten, M., Pokraev, S., & Koolwaaij, J. (2004). Context-aware recommendations in the mobile tourist application COMPASS. Proceedings of the 3rd International Conference on Adaptive Hypermedia and Adaptive Web-based System, 235-244.
Wang, Y., & Vassileva, J. (2004). Trust-based community formation in peer-to-peer file sharing networks. Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, 341-348.
Weng, L. (2009). On Recommending Tourist Attractions in a Mobile P2P Environment.