臺灣博碩士論文加值系統

　　隨著行動裝置與行動網路日益普及，人們可以隨時隨地存取的資訊激增；如何解決當前資訊超載（Information Overload）問題，並提供個性化推薦（Personalized Recommendation）服務是一項重要的研究議題。本論文利用Facebook開放圖（Open Graph）的打卡資料（Check-ins）設計一個行動餐廳推薦系統；以協同過濾法（Collaborative Filtering）為基礎，從個別使用者偏好總結團體偏好，實現團體推薦服務；並考慮社交圖（Social Graph）與情境資訊（Contextual Information）提升推薦品質；考慮的情境有位置、距離、年齡、性別指標、時間、星期、月份、同伴人數與同伴類型。本論文還設計一個方法，單獨評估位置與距離情境帶來的影響。
　　本論文的實驗資料是從Facebook徵集69名受測者，收集2010/8/15至2012/4/30期間，3928名使用者對2691家餐廳的8264次打卡。實驗結果顯示，本系統在中、長距離（3到5公里）的情境下，準確度相較於基於流行性推薦有顯著成長，成長率約38%。這意味著，如果使用者尋找餐廳所設定的範圍比較大，相較於基於流行性推薦，本系統可以產生更好的推薦結果。

With the increasing popularity of mobile devices and mobile networks, people can get a soaring amount of information, anywhere, anytime. How to solve the problem of the current information overload and provide personalized recommendation services is an important research topic. This thesis exploits the check-ins of Facebook Open Graph to design a mobile restaurant recommender system, which is based on collaborative filtering. The system summarizes the group preferences from individual users check-in in order to provide group recommendation services. Furthermore, the system considers social graph and contextual information to enhance the recommendation quality. These contextual information includes location, distance, age, sex index, time of day, weekday, month, number of companion and type of companion. In this thesis, we also proposed a method to evaluate the impact of location and distance context.
Our experimental data is collected from the 69 volunteers in Facebook, which includes the 8264 check-ins. These check-ins are contributed by 3928 users in 2691 different restaurants from 2010/8/15 to 2012/4/30. The experimental results reveal that the accuracy of our system can be increased by approximately 38% while suggest restaurants within the area of 3-5 km radius, compared to popularity-based recommendation. It means that the proposed system can provide better recommendations than popularity-based recommendations, if the user asks for a restaurant suggestion in a larger area.

致謝 I
中文摘要 II
英文摘要 III
目錄 V
圖目錄 VII
表目錄 VIII
1 導論 1
1.1 動機 1
1.2 目的 2
1.3 論文架構 2
2 背景與相關研究 3
2.1 情境感知（Context-aware） 3
2.2 社交圖與開放圖 4
2.2.1 社交圖（Social Graph） 4
2.2.2 開放圖（Open Graph） 5
2.2.3 打卡（Check in） 6
2.2.4 Graph API 7
2.3 推薦演算法 8
2.3.1 協同過濾法（Collaborative Filtering Approaches） 9
2.3.2 基於內容法（Content-based Approaches） 11
2.3.3 混合法（Hybrid Approaches） 11
2.3.4 結合情境之協同過濾法 12
2.4 行動餐廳推薦系統 13
3 使用社交圖與情境感知之行動餐廳推薦系統 14
3.1 資料定義 15
3.2 問題定義 16
3.3 解決方案 16
3.3.1 系統架構與資料模型 16
3.3.2 社交圖與情境感知推薦 18
3.3.3 社交圖與情境資訊比較 21
4 實作 25
4.1 雲端計算 25
4.2 系統流程 27
4.3 使用者介面 29
5 實驗與評價 31
5.1 資料收集 31
5.2 選擇相似度測量法 37
5.3 調整係數 41
5.4 評價 45
6 結論 49
6.1 總結貢獻 49
6.2 未來工作 50
參考文獻 51
附錄 53

1.刘建国, 周涛, and 汪秉宏, 个性化推荐系统的研究进展. 自然科学进展, 2009. 19(001): p. 1-15.
2.Schilit, B.N. and M.M. Theimer, Disseminating active map information to mobile hosts. Network, IEEE, 1994. 8(5): p. 22-32.
3.Dey, A.K., Understanding and using context. Personal and ubiquitous computing, 2001. 5(1): p. 4-7.
4.Woerndl, W. and J. Schlichter. Introducing context into recommender systems. in Proceedings of AAAI 2007 Workshop on Recommender Systems in e-Commerce. 2007.
5.Wikipedia contributors. Social graph. Available from: http://en.wikipedia.org/w/index.php?title=Social_graph&oldid=495500805.
6.Facebook. Open Graph. Available from: http://developers.facebook.com/docs/opengraph/.
7.Wikipedia contributors. Check-in. Available from: http://en.wikipedia.org/w/index.php?title=Check-in&oldid=495397467.
8.Facebook. Graph API. Available from: http://developers.facebook.com/docs/reference/api/.
9.Goldberg, D., et al., Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992. 35(12): p. 61-70.
10.Konstan, J.A., et al., GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 1997. 40(3): p. 77-87.
11.Adomavicius, G. and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 2005. 17(6): p. 734-749.
12.Sarwar, B., et al. Item-based collaborative filtering recommendation algorithms. in Proceedings of the 10th international conference on World Wide Web. 2001. ACM.
13.Claypool, M., et al. Combining content-based and collaborative filters in an online newspaper. in Proceedings of ACM SIGIR Workshop on Recommender Systems. 1999. Citeseer.
14.Pazzani, M.J., A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 1999. 13(5): p. 393-408.
15.Balabanović, M. and Y. Shoham, Fab: content-based, collaborative recommendation. Communications of the ACM, 1997. 40(3): p. 66-72.
16.Adomavicius, G., et al., Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 2005. 23(1): p. 103-145.
17.Chen, A., Context-aware collaborative filtering system: Predicting the user’s preference in the ubiquitous computing environment. Location-and Context-Awareness, 2005: p. 75-81.
18.Nguyen, Q.N. and F. Ricci. Long-term and session-specific user preferences in a mobile recommender system. in Proceedings of the 13th international conference on Intelligent user interfaces. 2008. ACM.
19.Sadeh, N., E. Chan, and L. Van. MyCampus: an agent-based environment for context-aware mobile services. in Proceedings of Workshop on Ubiquitous Agents on embedded, wearable and mobile devices. 2002.
20.黃啟嘉, 情境資訊對智慧型裝置上餐廳推薦系統的影響分析, in 臺灣大學資訊工程學研究所學位論文2009, 臺灣大學.
21.Park, M.H., H.S. Park, and S.B. Cho. Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. in Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction. 2008. Springer.
22.Wikipedia contributors. Cloud computing. Available from: http://en.wikipedia.org/w/index.php?title=Cloud_computing&oldid=499416499.
23.Sarwar, B., et al. Analysis of recommendation algorithms for e-commerce. in Proceedings of the 2nd ACM conference on Electronic commerce. 2000. ACM.
24.刘建国, et al., 个性化推荐系统评价方法综述. 复杂系统与复杂性科学, 2009. 6(003): p. 1-10.
25.Wikipedia contributors. Collaborative filtering. Available from: http://en.wikipedia.org/w/index.php?title=Collaborative_filtering&oldid=495504334.