此篇論文中提出一個嶄新的社群感知推薦方法，其運用公眾傾向和社群關係及影響來分類使用者對特定物件的喜好傾向。不同於傳統基於歷史資訊或是協同過濾等推薦方法，我們藉由網路上異質性的資料來源進行大量資訊收集，建立針對特定物件的大眾喜好傾向機率模型，應用在基於使用者特徵的喜好傾向分類。另外，我們以使用者為中心建構社群關係圖來分析社群成員之間的喜好傾向影響力，根據不同的社群網路平台所提供的互動模式，評估社群成員之間的影響程度，結合共同興趣的分析結果形成喜好傾向影響力。此外，我們也分析社群網路上特定群體當中成員之間的彼此喜好傾向影響機率，做為喜好機率調整標準。此篇論文的目的是利用大眾喜好推估個別使用者喜好傾向，再進一步考慮使用者社群中鄰居對他的喜好傾向影響力進行喜好機率調整，解決推薦系統對於新使用者無法取得歷史資訊進行分析的問題，在此方法中有兩個主要的優點，分別為社群關係在線上社群網路容易取得，以及此方法可以應用至任何對物件進行推薦的系統。在我們的實驗中，採用部落格、新聞及社群網站進行真實旅遊資訊收集與大眾喜好傾向模型建立，另外選擇臉書進行社群關係分析，實驗結果說明我們的方法不僅創新並可行。

A novel social-aware recommendation system framework which employs public preference and social influence to classify user preference orientation is proposed in this thesis. Unlike traditional content-based or collaborative filtering recommendation approaches, we collected large scale information from heterogeneous data sources to construct the public preference model for user’s feature-based preference orientation classification. Moreover, the social relation graph of target user is constructed to analyze social influence of preference between users in it. According to the different online social networks, corresponding types of interaction are adopted to estimate the degree of social influence between users. Then, the social influence of preference is calculated by social influence and interest similarity between users. In addition, the preference guiding pair is constructed by real data to be the baseline of preference adjustment. The purpose of this thesis is that using public preference to infer user preference and further adjusting user preference through social influence of preference from neighbors. The proposed method deals with the cold-start issue in recommendation system. There two main advantages of the proposed method are social relationship can be easily obtained from online social network and any type of recommendation system can be applied in the proposed method. In our experiment, blogs, news and online social networks are the information sources to construct public preference model. Moreover, Facebook, the most famous social media, is the platform selected for social relationship analysis. The experimental result shows our approach not only innovation but also practicable.

[Ado05] Adomavicius, G. and Tuzhilin, A. “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, Volume: 17, Issue: 6, pp. 734-749, 2005.
[Bal97] M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,” Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.
[Bre98] J.S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf. Uncertainty in Artificial Intelligence, pp. 43-52, July 1998.
[Bed11] Bedi, P. and Agarwal, S.K. “Preference Learning in Aspect-Oriented Recommender System,” International Conference on Computational Intelligence and Communication Networks (CICN), pp. 611-615, 2011.
[Chu01] In-Gook Chun and In-Sik Hong, “The implementation of knowledge-based recommender system for electronic commerce using Java expert system library,” IEEE International Symposium on Industrial Electronics, Volume: 3, pp.1766-1770, 2001.
[Chi11] Yu-Shian Chiu, Kuei-Hong Lin and Jia-Sin Chen, “A Social Network-based serendipity recommender system,” International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp.1-5, 2011.
[Cle11] M. Clements, P. Serdyukov, A. P. de Vries and M. J.T. Reinders, “Personalized Travel Recommendation based on Location Co-occurrence,” CoRR, abs/1106.5213, 2011.
[Del99] J. Delgado and N. Ishii, “Memory-Based Weighted-Majority Prediction for Recommender Systems,” Proc. ACM SIGIR Workshop Recommender Systems: Algorithms and Evaluation, 1999.
[Dia13] Diaby, M., Viennet, E. and Launay, T. “Toward the next generation of recruitment tools: An online social network-based job recommender system,” 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 821-828, 2013.
[Fu12] Meng-Hsuan Fu, Ling-Yu Chen, Kuan-Rong Lee and Yaw-Huang Kuo, “A Novel Opinion Analysis Scheme Using Social Relationships on Microblog,” Springer Netherlands, Future Information Technology, Application, and Service, Volume 1, pp.687-695, 2012.
[Gha10] Ghazanfar, M.A. and Prugel-Bennett, A. “A Scalable, Accurate Hybrid Recommender System,” Third International Conference on Knowledge Discovery and Data Mining (WKDD), pp. 94-98, 2010.
[Hof03] T. Hofmann, “Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis,” Proc. 26th Ann. Int’l ACM SIGIR Conf., pp.259-266, 2003
[Hor06] T. Horozov, N. Narasimhan and V. Vasudevan, “Using Location for Personalized POI Recommendations in Mobile Environments,” in SAINT, pp. 124-129, 2006.
[Hua09] Y. Huang and L. Bian, “A Bayesian Network and Analytic Hierarchy Process Based Personalized Recommendations for Tourist Attractions over the Internet,” Expert Systems with Applications, 36(1), pp. 933-943, 2009.
[Lee09] C.-S. Lee, Y.-C. Chang and M.-H. Wang, “Ontological Recommendation Multi-Agent for Tainan City Travel,” Expert Systems with Applications, 36(3), pp. 6740-6753, 2009.
[Lia10] Yu Liangxing and Dong Aihua, “Hybrid Product Recommender System for Apparel Retailing Customers,” 2010 WASE International Conference on Information Engineering (ICIE), Volume: 1, pp.356-360, 2010.
[Lu10] X. Lu, C. Wang, J.-M. Yang, Y. Pang and L. Zhang, “Photo2Trip: Generating Travel Routes from Geo-Tagged Photos for Trip Planning,” in ACM MM, pp. 143-152, 2010.
[Lu12] Eric Hsueh-Chan Lu, Ching-Yu Chen and Vincent S. Tseng. “Personalized trip recommendation with multiple constraints by mining user check-in behaviors,” Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 209-218, 2012.
[Mar03] B. Marlin, “Modeling User Rating Profiles for Collaborative Filtering,” Proc. 17th Ann. Conf. Neural Information Processing Systems (NIPS), 2003.
[Mor14] Kimberlee Morrison, “The Growth of Social Media: From Passing Trend to International Obsession,” http://socialtimes.com/, 2014.

[Paz99] M. Pazzani, “A Framework for Collaborative, Content-Based, and Demographic Filtering,” Artificial Intelligence Rev, pp. 393-408, 1999.
[Pav02] D. Pavlov and D. Pennock, “A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains,” Proc. 16th Ann. Conf. Neural Information Processing Systems (NIPS), 2002.
[Ye10] M. Ye, P. Yin and W.-C. Lee, “Location Recommendation for Location-Based Social Networks,” in ACM GIS, pp. 458-461, 2010.
[Zhe11] Y. Zheng and X. Xie, “Learning Travel Recommendations from User-Generated GPS Traces,” ACM Trans. on Intelligent Systems and Technologies, v.2 n.1, p.1-29, 2011.
[Zho11] C. Zhou and X. Meng, “STS Complex Spatio-Temporal Sequence Mining in Flickr,” In DASFAA, pp. 208-223, 2011.