隨著資訊的傳播，人們越來越重視美妝保養。但隨著美妝保養資訊的大量增加，造成使用者資訊過載，使用者無法消化龐大資訊，且過去在此領域中並無相關之資訊過濾系統協助使用者過濾出符合其需求之資訊，因此使用者需要一資訊過濾系統協助取得符合其需求的美妝保養資訊。
又因美妝保養產品的使用特性，使得大部分消費者購買美妝保養產品時，不會單獨購買其中一類產品，而是會按其保養程序，購買不同類別的產品搭配使用。這樣的消費特性使得美妝保養產品具有集合的性質，因此在進行產品推薦時，以產品集合的形式進行推薦會較符合使用者需求。
為了能夠有效且正確的推薦美妝保養社群網站的使用者可能適合其膚質的美妝保養產品集合，本研究蒐集美妝保養社群網站上之資料，設計一適用於美妝保養領域的混合式產品集合推薦系統：以使用者特性及其使用過之產品，利用餘弦相似度計算使用者間相似度，並利用K-medoids分群法進行分群。分群之後再蒐集群內使用者使用過的產品，建立產品類別的連結，找出該群使用者所使用的產品類別關係，以建立產品集的雛形。有了產品類別關係後便可以如以往的研究一樣針對每一類別之產品進行推薦。除此之外，本研究利用使用者撰寫過之心得評論進行分析，取得群內使用者對於產品之評分後，對產品進行排序，找出前n個產品作為推薦清單，進而進行跨類別美妝保養產品集合推薦，以協助使用者省去過濾資訊的時間並推薦出符合其特質之產品。實驗結果發現，當演算法門檻值設為2 log_2⁡n時能在分群效率與品質間取得良好的平衡；而推薦產品集合數量為一組時表現最佳。

Due to the amount of beauty and care information increases, people need a recommender system to help users to make decisions. Also, users of beauty and care products won’t only use single products but use different products which are cross many categories. The existing recommendation systems only recommend user products related to the target user or product. The aim of this study is to design a method which can recommend users a set of products and fit their needs.
This study propose a recommender system by using K-medoids cluster approach. When calculating users’ similarity, this study uses cosine similarity for calculation. The system will recommend target user the product set from other users which are similar to the target user. The product recommendation will use the times that product be used and sentiment score to help get the better recommendation.
The result of the recommendation will be evaluated by the precision and hit-rate. According to the experiment, the cluster algorithm will reduce time cost by adding a threshold 2 log_2⁡n, and the product set recommendation will get better performance on the dataset when only recommend one product set.

Anand, D., & Bharadwaj, K. K. (2010). Enhancing Accuracy of Recommender System through Adaptive Similarity Measures Based on Hybrid Features. In N. T. Nguyen, M. T. Le & J. Świątek (Eds.), Intelligent Information and Database Systems: Second International Conference, ACIIDS, Hue City, Vietnam, March 24-26, 2010. Proceedings, Part II (pp. 1-10). Berlin, Heidelberg: Springer Berlin Heidelberg.
Barragáns-Martínez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-López, M., Mikic-Fonte, F. A., & Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, 180(22), 4290-4311.
Cambria, E., Schuller, B., Yunqing, X., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. Intelligent Systems, IEEE, 28(2), 15-21.
Chang, P.-C., Galley, M., & Manning, C. D. (2008). Optimizing Chinese word segmentation for machine translation performance. Paper presented at the Proceedings of the Third Workshop on Statistical Machine Translation, Columbus, Ohio.
Chen, K.-J., & Bai, M.-H. (1998). Unknown Word Detection for Chinese by a Corpus-based Learning Method. Computational Linguistics and Chinese Language Processing, 3(1), 27-44.
Chen, K.-J., & Liu, S.-H. (1992). Word identification for Mandarin Chinese sentences. Paper presented at the Proceedings of the 14th conference on Computational linguistics - Volume 1, Nantes, France.
Chen, K.-J., & Ma, W.-Y. (2002). Unknown word extraction for Chinese documents. Paper presented at the Proceedings of the 19th international conference on Computational linguistics - Volume 1, Taipei, Taiwan.
Chiang, H.-S., & Huang, T.-C. (2015). User-adapted travel planning system for personalized schedule recommendation. Information Fusion, 21, 3-17.
Deshpande, M., & Karypis, G. (2004). Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst., 22(1), 143-177.
Fu, G., & Luke, K.-K. (2005). Chinese named entity recognition using lexicalized HMMs. SIGKDD Explor. Newsl., 7(1), 19-25.
Garcia Esparza, S., O’Mahony, M. P., & Smyth, B. (2012). Mining the real-time web: A novel approach to product recommendation. Knowledge-Based Systems, 29, 3-11.
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On Clustering Validation Techniques. Journal of Intelligent Information Systems, 17(2-3), 107-145.
Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2016). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal.
Kumar, N. P., & Fan, Z. (2015). Hybrid User-Item Based Collaborative Filtering. Procedia Computer Science, 60, 1453-1461.
Levy, R., & Manning, C. (2003). Is it harder to parse Chinese, or the Chinese Treebank? Paper presented at the Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, Sapporo, Japan.
Liang, T.-P., Yang, Y.-F., Chen, D.-N., & Ku, Y.-C. (2008). A semantic-expansion approach to personalized knowledge recommendation. Decision Support Systems, 45(3), 401-412.
Liu, D.-R., & Shih, Y.-Y. (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. Journal of Systems and Software, 77(2), 181-191.
Ma, W.-Y., & Chen, K.-J. (2003). A bottom-up merging algorithm for Chinese unknown word extraction. Paper presented at the Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17, Sapporo, Japan.
Madhulatha, T. (2011). Comparison between K-Means and K-Medoids Clustering Algorithms. In D. Wyld, M. Wozniak, N. Chaki, N. Meghanathan & D. Nagamalai (Eds.), Advances in Computing and Information Technology (Vol. 198, pp. 472-481): Springer Berlin Heidelberg.
Mahdavi, M., Chehreghani, M. H., Abolhassani, H., & Forsati, R. (2008). Novel meta-heuristic algorithms for clustering web documents. Applied Mathematics and Computation, 201(1–2), 441-451.
Mishra, R., Kumar, P., & Bhasker, B. (2015). A web recommendation system considering sequential information. Decision Support Systems, 75, 1-10.
Morente-Molinera, J. A., Pérez, I. J., Ureña, M. R., & Herrera-Viedma, E. (2016). Creating knowledge databases for storing and sharing people knowledge automatically using group decision making and fuzzy ontologies. Information Sciences, 328, 418-434.
Quan, C., & Ren, F. (2014). Unsupervised product feature extraction for feature-oriented opinion determination. Information Sciences, 272, 16-28.
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14-46.
Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Commun. ACM, 18(11), 613-620.
Teahan, W. J., McNab, R., Wen, Y., & Witten, I. H. (2000). A compression-based algorithm for Chinese word segmentation. Comput. Linguist., 26(3), 375-393.
UrCosme. (2015). UrCosme. from https://www.urcosme.com/
WebMD. (2015). Skin Types and Care: Normal, Dry, Oily, Combi... from http://www.wikihow.com/Determine-Your-Skin-Type
Wikipedia. (2015). Cosmetics. from https://en.wikipedia.org/wiki/Cosmetics#Types
Wong, P.-k., & Chan, C. (1996). Chinese word segmentation based on maximum matching and word binding force. Paper presented at the Proceedings of the 16th conference on Computational linguistics - Volume 1, Copenhagen, Denmark.
Wu, Z., & Tseng, G. (1993). Chinese text segmentation for text retrieval: Achievements and problems. Journal of the American Society for Information Science, 44(9), 532-542.
Xue, N., Chiou, F.-D., & Palmer, M. (2002). Building a large-scale annotated Chinese corpus. Paper presented at the Proceedings of the 19th international conference on Computational linguistics - Volume 1, Taipei, Taiwan.
Yang, C. C., Luk, J. W. K., Yung, S. K., & Yen, J. (2000). Combination and boundary detection approaches on Chinese indexing. Journal of the American Society for Information Science, 51(4), 340-351.
Yu, Z., Xu, H., Yang, Z., & Guo, B. (2016). Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints. IEEE Transactions on Human-Machine Systems, 46(1), 151-158.
Zahra, S., Ghazanfar, M. A., Khalid, A., Azam, M. A., Naeem, U., & Prugel-Bennett, A. (2015). Novel centroid selection approaches for KMeans-clustering based recommender systems. Information Sciences, 320, 156-189.
Zhang, L., Hu, C., Chen, Q., Chen, Y., & Shi, Y. (2012). Domain Knowledge Based Personalized Recommendation Model and Its Application in Cross-selling. Procedia Computer Science, 9, 1314-1323.
FashionGuide華人第一女性時尚美妝傳媒. (2015). FashionGuide華人第一女性時尚美妝傳媒. from http://www.fashionguide.com.tw/
InsightXplorer創市際市場研究顧問. (2015a). 美妝保養調查及台灣美妝相關網站使用概況. 創市際雙週刊, 38.
InsightXplorer創市際市場研究顧問. (2015b). 美妝網站調查與 美容時尚類別網站使用概況.
王京盛. (2012). 考量語意及引用分析之研究主題趨勢分析方法. 國立成功大學.
吳虹瑩. (2012). 朋友圈餐廳推薦機制之研究. 輔仁大學, 新北市.
林宜瑩. (2010). 利用時間因子與名詞片語之文獻主題追蹤法. 國立成功大學.
高照明. (2012). 語料庫建構技術研究報告.
瑞麗美人國際媒體. (2014). 四個使用保養品的正確觀念，打造陶瓷肌！. Retrieved May,22, 2016, from http://www.babyou.com/opencms/channel3/Babyou018376.html?__locale=zh_TW
資策會產業情報研究所. (2014). 「網路社群使用」調查. Retrieved March.22, 2016, from http://mic.iii.org.tw/intelligence/pressroom/pop_pressfull.asp?sno=366&type1=2
維基百科. (2015a, 2016/3/21). 社群媒體. Retrieved May.22, 2016, from https://zh.wikipedia.org/wiki/%E7%A4%BE%E4%BC%9A%E5%8C%96%E5%AA%92%E4%BD%93
維基百科. (2015b). 社群網路服務. Retrieved May.22, 2016, from https://zh.wikipedia.org/wiki/%E7%A4%BE%E4%BA%A4%E7%B6%B2%E8%B7%AF%E6%9C%8D%E5%8B%99
維基百科. (2015c). 虛擬社群. Retrieved May.22, 2016, from https://zh.wikipedia.org/wiki/%E8%99%9B%E6%93%AC%E7%A4%BE%E7%BE%A4
中央研究院中文詞知識庫小組. (2011). 廣義知網知識本體架構.
圖書館學與資訊科學大辭典. (2012).