電子商務系統逐漸成為現今企業界與顧客之間重要的系統及交易平台。推薦系統可以針對顧客需求做出最好的產品推薦，無疑是在眾多的電子商務應用中最為廣泛運用的工具。然而，即使文獻上已經存在許多推薦系統相關的研究，卻尚未有學者提出一套可將推薦系統三部份整合的分析模組；此三部份分別為推薦系統的輸入，推薦系統所採用的推薦機制，以及推薦輸出。另外，許多推薦系統也都面臨到顧客與產品資料不足而導致無法有效推薦的問題。除此之外，目前的文獻研究往往著重於推薦績效的考量，而忽略電子商務環境下之另一重要議題，亦即供應商利潤。因此為了解決上述議題，本研究提出了策略性導向推薦模組。在本研究模組中提出了三個子模組：輸入、關聯性預測以及輸出模組；在此三個子模組中提出了三套相關工具，分別是以期望最大化演算法為基礎之穩健主成份分析法、利用群組效益的協同過濾機制以及策略分析模型。我們將所提出推薦模組應用在多個實驗以及台灣3C零售商案例的研究，此推薦模組亦從研究結果中證明了可對於企業創造相當的績效。

Electronic Commerce (EC) has become an important support for business and is regarded as an efficient system that connects suppliers with online users. Among the applications of EC, a Recommender System (RS) is undoubtedly a popular approach for promoting the products actively to the users. Even if many approaches have been proposed, a comprehensive module comprising of essential sub-modules of input profiles, a recommendation scheme, and an output interface of recommendations in the RS is still lacking. Besides, many approaches are confronted with the cold-start problem, which can be attributed to the problem of sparse user-item matrices. In addition, the fundamental issue of profit consideration for an EC company is not addressed in general terms. Therefore, this thesis aims to construct an RS with a strategy-oriented operation module regarding the above aspects; and with this module, three sub-modules of input, association prediction and output are proposed along with three tools of the Expectation-Robust Principal Component Analysis (E-RPCA), the Clique-Effects Collaborative Filtering (CECF), and the Strategy Analysis Model (SAM). The proposed RS module has been implemented by several experiments and the case studies of 3C retailers in Taiwan; promising results were obtained to approve the contributions of the proposed module.

[1] Adams, E., Walczak, B., Vervaet, C., Risha, P. G., and Massart, D. L., (2002). Principal component analysis of dissolution data with missing elements. International Journal of Pharmaceutics, 234, 1–2, 169–178.
[2] Aggarwal, C. C., Wolf, J. L., and Yu, P. S., (1999). A new method for similarity indexing of market basket data. SIGMOD Conference, 407–418.
[3] Agrawal, R., Imielinski, T., and Swami, A., (1993). Mining association rules between sets of items in large databases. In Proc. Conf. Management of Data, ACM SIGMOD, Washington DC, 207–216.
[4] Aitchison, J., (1983). Principal component analysis of compositional data. Biometrika, 70, 1, 57–65.
[5] Bacon-Shone, J., (1992). Ranking methods for compositional data. Applied Statistics, 41, 3, 533–537.
[6] Baxter, M. J., (1993) Principal component analysis of ranked compositional data: an empirical study. Research Report 17/93, Department of Mathematics, Statistics and Operational Research, Nottingham Trent University.
[7] Bell, R. M., Koren, Y., and Volinsky, C., (2007). Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD International Coference on Knowledge Discovery and Data Mining, San Jose, California, USA, 95–104.
[8] Billsus, D., and Pazzani, M., (1998). Learning collaborative information filters. In J. Shavlik (ed.), 15th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, California, USA, 46–54.
[9] Breese, J. S., Heckerman, D., and Kadie, C., (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of the 14th Conf. Uncertainly in Artificial Intelligence, 43–52.
[10] Burke, R., (2002). Hybrid recommender systems: survey and experiments. User Modeling and User-Adapted Interaction, 12, 331–370.
[11] Changchien, S. W., Lee, C. F., and Hsu, Y. J., (2004). On-line personalized sales promotion in electronic commerce. Expert Systems with Applications, 27, 35–52.
[12] Chen, H., (2002). Principal component analysis with missing data and outliers. Technical Report, Electrical and Computer Engineering Department Rutgers University, Piscataway, New Jersey, 08854.
[13] Chen, L. S., Hsu, F. H., Chen, M. C., and Hsu, Y. C., (2008). Developing recommender systems with the consideration of product profitability for sellers. Information Sciences, 178, 1032–1048.
[14] Chen, L., and Sycara, K., (1998). Webmate: A personal agent for browsing and searching. In Proc. of 2nd Conf. Autonomous Agents, 132–139.
[15] Chen, L.S., Hsu, F.H., Chen, M. C., and Hsu, Y. C., (2008). Developing recommender systems with the consideration of product profitability for sellers. Information Sciences, 178, 1032–1048.
[16] Cho, Y. H., and Kim, J. K., (2004). Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26, 233–246.
[17] Dempster, A. P., Laird, N. M., and Rubin, D. B., (1977). Maximum likelihood for incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39, 1, 1–38.
[18] Deshpande, M., and Karypis, G., (2004). Item-based top-N recommendation algorithms. ACM Transactions on Information Systems, 22, 1, 143–177.
[19] Eben-Chaime, M. (1996). Parametric solution for linear bi-criteria knapsack. Management Science, 42, 1565–1575.
[20] Eisner, M.J., and Severance, D.G. (1976). Mathematical techniques for efficient record segmentation in large shared database. Association of Computing Machinery, 23, 619–735.
[21] Geoffrion, A. M., and Nauss, R. (1979). Parametric and postoptimality analysis in integer linear programming, Management Science, 23, 453–466.
[22] Goldberg, K., Roeder, T., Guptra, D., and Perkins, C., (2001). Eigentaste: a constant-time collaborative filtering algorithm. Information Retrieval, 4, 2, 133–151.
[23] Grung, B., and Manne, R., (1998). Missing values in principle component analysis. Chemometrics and Intelligent Laboratory Systems, 42, 1–2, 125–139.
[24] Guo, J., (2007). Business-to-business electronic market place selection. Enterprise Information Systems, 1, 4, 383–419.
[25] Guo, J., (2009). Collaborative conceptualization: towards a conceptual foundation of interoperable electronic product catalogue system design. Enterprise Information Systems, 3, 1, 59–94.
[26] Hanani, U., Shapira, B., and Shoval, P., (2001). Information filtering: overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11, 3, 203–259.
[27] Herlocker, J. L., Konstan J. A., Terveen, L. G., and Riedl J. T., (2004). Evaluating collaborative filtering recommender systems. ACM Transaction Information Systems, 22, 1, 5–53.
[28] Hogg, T., (2010). Inferring preference correlations from social networks, Electronic Commerce Research and Applications, 9, 1, 29–37.
[29] Hsu, P. Y., Chen, Y. L., and Ling, C. C., (2004). Algorithms for mining association rules in bag databases. Information Sciences, 166, 31–47.
[30] Huber, P. J., (1985). Projection pursuit. The Annals of Statistics, 13, 2, 435–475.
[31] Hubert, M., Rousseeuw, P. J., and Vanden Branden, K., (2005). ROBPCA: a new approach to robust principal components analysis. Technometrics, 47, 1, 64–79.
[32] Johnson, R. A., and Wichern, D. W., (2002). Applied multivariate statistical analysis, 5th edition. Prentice Hall, New Jersey, USA.
[33] Jolliffe, I. T., (2002). Principal component analysis, 2nd edition. Springer Series in Statistics, New York, NY.
[34] Julià, C., Sappa, A. D., Lumbreras, F., Serrat, J., and López, A., (2009). Predicting missing ratings in recommender systems: adapted factorization approach. International Journal of Electronic Commerce, 14, 2, 89–108.
[35] Karypis, G., (2001). Evaluation of item-based top-N recommendation algorithms. In Proceedings of the 10th international Conference on Information and Knowledge Management, Atlanda, Georgia, USA, 247–254.
[36] Kaufman, L., Rousseeuw, P. J., (1990). Finding groups in data. an introduction to cluster analysis. Wiley, New York.
[37] Kim, B. M., Li, Q., Park, C. S., Kim, S. G., and Kim, J. Y., (2006). A new approach for combining content-based and collaborative filters. Journal of Intelligent Information System, 27, 1, 79–91.
[38] Kim, D., and Yum, B. J., (2005). Collaborative filtering based on iterative principal component analysis. Expert Systems with Applications, 28, 4, 823–830.
[39] Kim, H. T., Ji, A. T., Kim, Ha. I., and Jo, G. S., (2010). Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electronic Commerce Research and Applications, 9, 1, 73–83.
[40] Kohrs A., and Meriadlo B., (2001). Creating user adapted websites by the use of collaborative filtering. Interacting with Computers, 13, 695–716.
[41] Konstan, J. A., Riedl, J., Borchers, A., and Herlocker, J. L., (1998). Recommender systems: a grouplens perspective. In: Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08). Menlo Park, CA: AAAI Press, 60–64
[42] Lam, C., (2004). SNACK: Incorporating social network information in automated collaborative filtering. In Proceedings of the Fifth ACM Conference on Electronic Commerce (EC’04). ACM Press, 254–255.
[43] Li, H., and Wang, H., (2007). A multi-agent-based model for a negotiation support system in electronic commerce. Enterprise Information Systems, 1, 4, 457–472.
[44] Linden G., Smith B., and York J., (2003). Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7, 1, 76–80.
[45] Little, R. J. A., (1988). Robust estimation of the mean and covariance matrix from data with missing values. Applied Statistics, 37, 1, 23–38.
[46] Liu, D. R., and Shih, Y. Y., (2005). Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. The Journal of Systems and Software, 77, 181–191.
[47] Liu, D. R., and Shih, Y. Y., (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value. Information and Management, 42, 387–400.
[48] Martello, S., and Tooth, P., (1990). Knapsack problems: algorithms and computer implementations. In: Series in Discrete Mathematics and Optimization, Wiley Inter Sciences, New York.
[49] McPherson, M., Smith-Lovin, L., and Cook, J. M., (2001). Birds of a feather: homophily in social networks. Annual Review of Sociology, 27, 415–444.
[50] Mild, A., and Reutterer, T., (2001). Collaborative filtering methods for binary market basket data analysis. Lecture Notes in Computer Science, 2252, 302–313.
[51] Mild, A., and Reutterer, T., (2003). An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing and Consumer Services, 10, 123–133.
[52] Montaner, M., López, B., and Rosa, J. L. D. L., (2003). A taxonomy of recommender agents on the Internet. Artificial Intelligence Review, 19, 285–330.
[53] Mooney, R. J., and Roy, L., (2000). Content-based book recommending using learning for text categorization. In Proc. of 5th ACM conf. on Digital libraries, 195–204.
[54] Papagelis, M., Plexousakis, D. and Kutsuras, T., (2005). Alleviation the sparsity problem of collaborative filtering using trust inferences. In Proceedings of the 3rd International Conference on Trust Management, Paris, France, 224–239.
[55] Phillips, Charles, and Meeker M., (2000). The B2B Internet report: collaborative commerce. Morgan Stanley Dean Witter Equity Research.
[56] Raghavan, V. V., and Wong, S. K. M., (1986). A critical analysis of vector space model for information retrieval. Journal of the American Society for Information Science, 37, 279–87.
[57] Rousseeuw, P. J., and Van Driessen, K., (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41, 3, 212–223.
[58] Russell, G. J., and Petersen, A., (2000). Analysis of cross category dependence in market basket selection. Journal of Retailing, 76, 367–392.
[59] Sarwar, B. M., Karypis G., Konstan, J. A., and Riedl J., (2000). Analysis of recommendation algorithms for E-commerce. In Proc. of 2nd ACM EC, 158–167.
[60] Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. T., (2000). Application of dimensionality reduction in recommender system – a case study. In Proceedings of ACM WEBKDD Workshop, Boston, MA, USA.
[61] Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., and Riedl, J., (1998). Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In Proc. of CSCW, Seattle, Washington.
[62] Schafer, J. B., Konstan, J. A., and Riedl, J. T., (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5, 1–2, 115–153.
[63] Schafer, J. B., Konstan, J. A., and Riedl, J. T., (1999). Recommender systems in e-commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce, Denver, Colorado, USA, 1999, 158–166.
[64] Schafer, J. B., Konstan, J. A., and Riedl, J., (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5, 115–153.
[65] Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M., (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th ACM SIGIR on Research and Development in Information Retrieval, Tampere, Finland, 253–260.
[66] Serneels, S., and Verdonck, T., (2008). Principal component analysis for data containing outliers and missing elements. Computational Statistics and Data Analysis, 52, 3, 1712–1727.
[67] Shlens, J., (2009). A tutorial on principal component analysis. Center for Neural Science, New York University.
[68] Stanimirova, I., Daszykowski, M., and Walczak, B., (2007). Dealing with missing values and outliers in principal components analysis, Talanta, 72, 1, 172–178.
[69] The Economist, (1997). Hands off the Internet. The Economist, 344(8024), 15–16. July 5.
[70] Theodoridis, S., and Koutroumbas, K., (2003). Pattern recognition. Academic Press.
[71] Verboven, S., and Hubert, M., (2005). LIBRA: a MATLAB library for robust analysis. Chemometrics and Intelligent Laboratory Systems, 75, 2, 127–136.
[72] Walczak, B., and Massart, D. L., (2001). Dealing with missing data part I. Chemometrics and Intelligent Laboratory Systems, 58, 1, 15–27.
[73] Wang, H. F., and Wu, C. T., (2009). A mathematical model for product selection strategies in a recommender system, Expert Systems with Applications, 36, 3, 7299–7308.
[74] Wang, H. F., and Wu, C. T., (2010). A strategy-oriented operation module for recommender systems in E-commerce. Computers & Operations Research, doi:10.1016/j.cor.2010.03.011.
[75] Wang, H. F., and Wu, C. T., (2011). Resolution of a sparse user-item matrix in recommender systems by E-RPCA, submitted to Electronic Commerce Research and Applications, ECRA-D-10-00275.
[76] Wang, S., and Archer, N. P., (2007). Electronic marketplace definition and classifications: literature review and clarifications. Enterprise Information Systems, 1, 1, 89–112.
[77] Xu, X., (2008). Effects of two national environmental factors on e-commerce functionality adoption: a cross-country case study of a global bank. Enterprise Information Systems, 2, 3, 325–339.
[78] Zeng, C., Xing, C. X., Zhou, L. Z., and Zheng, X. H., (2004). Similarity measure and instance selection for collaborative filtering. International Journal of Electronic Commerce, 8, 4, 115–129.