線上市集的競爭非常激烈且瞬息萬變，許多企業為了獲持續得競爭優勢，提供了多種的線上的服務與應用程式。然而，開發高品質的服務和應用程式來滿足使者的需求和喜好，供應商必須要了解和預測其使用者之交易行為。因為使用者使用不同的系統進行互動，而使得這些行為有所不同，例如，由不同供應商提供的線上購物、網路拍賣等系統。
本預測方法建立於內文資訊(可見的評分資料)或是交易數據(隱藏性的回應)中。然而，要求使用者提供內文資訊和明確的回應，同時更新關於個人的資訊與偏好，在實務上，這些非常困難。例如，在某些個案中， B2B的即時價格預測模型反而與線上拍賣結果相左，甚至內文資訊對於預測的貢獻更少。因此，本研究旨在開發有效的預測方法，此方法以使用者的交易資料(隱藏性的回應) 為基礎，當中包含了購買和議價資料，並提出了兩種預測方法。
第一種的預測方法，試圖從B2B 拍賣的收盤價和時間反向推估預測，而第二種方法預測使用者在電子商務中未來的偏好且提供建議項目。第一種方法的模擬是以 B2B線上拍賣市場的即時資料為基，時間超過兩年。模擬結果顯示，在觀察的第4次出價，此方法可以預測到收盤價格的84.6％，且準確率達71.9％。為了評估的第二個預測方法的效率和穩定性而進行了多次的實驗，資料來自於向3G服務供應商購買電影的網站。實驗結果顯示，第二種方法與傳統以內容為基礎的方法相比較，準確性平均提高了1.15倍。

In very competitive and fast changing online marketplaces, many companies have been providing various amounts of online services and applications to gain sustainable competitive advantages. However, to develop high quality services and applications which meet users requirements and preferences, providers always need to know and predict their users transaction behavior. These behaviors vary from systems since users interact with different systems e.g. online shopping, online auction etc., that hosted by different providers.
The prediction approaches can be developed based on whether contextual information/explict ratings or transactional data/implicit feedbacks. However, in practice, since contextual information and explicit feedbacks are obtained by asking users to provide and update information about their identity and interests, this information is difficult to collect and not always available. In some cases, for example, in real time closing price prediction model for business–to-business (B2B) reverse online auction, even the contextual information less contributes for prediction. Therefore, this study aims to develop efficient prediction approaches based on user’s transactional data/implicit feedback which are purchasing and bidding data and proposes two prediction approaches.
The first prediction approach strives to predict closing price and duration of B2B reverse online auction, while second approach predicts user’s future preference for recommending items in m-commerce. The simulation of the first approach is based on real time auction data derived from a B2B online auction marketplace over a two-year period. Simulation results show that after observing the first 4 bids, this methodology can predict closing prices and duration with 84.6 % and 71.9 % accuracy, respectively. To assess the efficiency and stability of the second prediction approach, the several experiments are conducted on purchasing data collected from a movie website hosting by a 3G service provider. The experimental results show that the second approach improves the accuracy of traditional content-based approach by 1.15 times on average.

(Ardissono et al. 2002) Ardissono, L., Goy, A., Petrone, G., and Segnan, M., 2002. Personalization in Business-to-Customer Interaction, Communications of the ACM - The Adaptive Web, 45: 5
(Adomavicius & Tuzhilin, 2005) 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
(Adomavicius et al. 2005) Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A., 2005. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach, ACM Trans. Information Systems, 23:1
(Albadvi & Shahbazi, 2009) Albadvi, A., and Shahbazi, M., 2009. A hybrid recommendation technique based on product category attributes. Expert Systems with Applications 36(9): 11480-11488
(Anil et al. 1997) Anil, M., Moschis, G.P., Lee, E., 1997. A longtitudinal study of the effects of life status changes on changes in consumer preferences, J. of the Acad. Mark. Sci 36:234-246
(Armstrong, 2001) Armstrong, J.S., 2001. Principles of Forecasting—A Handbook for Researchers and Practitioners. Kluwer Academic
(Beer, 2000) Beer R.D., 2000. Dynamical approaches to cognitive science, Trends in cognitive sciences, 4(3): 91–99
(Balabanovic & Shoham, 1997) Balabanovic, M., Shoham, Y., 1997. Fab: Content-based, Collaborative Recommendation. Communications of the Association for Computing Machinery, 40(3): 66–72.
(Bennett & Lanning, 2007) Bennett. J., Lanning. S., 2007. The Netflix Prize, KDD Cup and Workshop, www.netflixprize.com
(Bondu et al. 2010) Bondu, A., Boulle, M., Lemaire, V., 2010. A non-parametric semi-supervised discretization method. Knowl Inf Syst, 24(1):35–57
(Buss, 2003) Buss, D. M., 2003 Evolutionary psychology: The new science of the mind (2nd ed.). Boston: Allyn & Bacon
(Bargh et al. 1996) Bargh, J.A., Chen, M., Burrows, L., 1996. Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71 (2): 230–244.
(Carter et al. 2004) Carter, C.R., Kaufmann, L., Beall, S., et al. 2004. Reverse-auctions - grounded theory from the buyer and supplier perspective. Transport Res E-Log, 40: 229-254
(Claycomba et al. 2005) Claycomba, C., Iyerb, K., Germainc R., 2005. Predicting the level of B2B e-commerce in industrial organizations, Industrial Marketing Management, 34: 221– 234
(Chen et al. 2005) Chen, M-C., Chiu, A-L., Chang, H-H., 2005. Mining changes in customer behavior in retail marketing, Expert Systems with Applications 28: 773-781
(Deshpande & Karypis, 2004) Deshpande, M., Karypis, G., 2004. Item-Based Top-N Recommendation Algorithms, Journal ACM Transactions on Information Systems, 22(1): 143-177
(Ding et al. 2006) Ding, Y., Li, X., Orlowska, M.E., 2006. Recency-Based Collaborative Filtering In: Proceeding ADC ''06 Proceedings of the 17th Australasian Database Conference
(Ding & Li, 2005) Ding, Y., Li, X., 2005. Time Weight Collaborative Filtering, In: Proceeding CIKM ''05 Proceedings of the 14th ACM international conference on Information and knowledge management
(Drolet & Morris, 2000) Drolet, A.L., Morris, M.W., 2000. Rapport in conflict resolution: Accounting for how nonverbal exchange fosters cooperation on mutually beneficial settlements to mixed-motive conflicts. Journal of Experimental Social Psychology 36 (1), 26–50.
(Gibson, 2000) Gibson, R., 2000. Intercultural Business Communication. Cornelsen and Oxford, Berlin
(Ghani & Simmons, 2004) Ghani, R., Simmons, H., 2004. Predicting the end-price of online auctions, In: Proceedings of International Workshop on Data Mining and Adaptive Modeling Methods for Economics and Management, Pisa, Italy
(Hannon et al. 2010) Hannon, J., Bennett M., Smyth, B., 2010. Recommending Twitter Users to Follow Using Content and Collaborative Filtering Approaches, In: Proceeding RecSys2010, Barcelona, Spain.
(Herrera et al. 2008) Herrera, F., Herrera-Viedma, E., Martinez, L., 2008. A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE T Fuzzy Syst 16(2): 354–370
(Hong & Shum, 2002) Hong, H., Shum, M., 2002. Increasing competition and the Winner’s curse: Evidence from procurement. Review of Economic Studies 69 (4), 871–898
(Huang & Huang, 2009) Huang, C.-L., Huang W.-L., 2009. Handling sequential pattern decay: Developing a two-stage collaborative recommender system. Electronic Commerce research and Applications 8:117-129
(Jin et al. 2009) Jin, R., Breitbart, Y., Muoh, C., 2009. Data discretization unification. Knowl Inf Syst 19(1):1–29
(Karypis, 2001) Karypis, G., 2001. Evaluation of Item-Based Top-N Recommendation Algorithms, In: Proceeding CIKM ''01 Proceedings of the tenth international conference on Information and knowledge management
(Kianmehr et al. 2010) Kianmehr, K., Alshalalfa, M., Alhajj, R., 2010. Fuzzy clustering-based discretization for gene expression classification. Knowl Inf Syst. 24(3): 441-465
(Kolter & Maloof, 2003) Kolter, J.Z., Maloof, M.A., 2003. Dynamic weighted majority: A new ensemble method for tracking concept drift. In: Proceedings of the IEEE Conference on Data Mining (ICDM’03), 123–130
(Konstan et al. 1997) Konstan, J.A., Miller, B.N., Maltz, D., et al., 1997. GroupLens: Applying Collaborative Filtering to Usenet News, Comm. ACM, 40 (3):77-87
(Koren et al. 2009) Koren, Y., Bell, R., Volinsky, C., 2009. Matrix factorization techniques for recommender systems. IEEE Comput, 42: 30–37
(Koren, 2010) Koren, Y., 2010, Collaborative filtering with temporal dynamics, Communications of the ACM, 53(4),
(Laseter & Evans, 2001) Laseter, T., Evans, D., 2001. Beating the B2B Odds. Strategy+Business Issue 23, Second Quarter
(Lee & Park, 2007) Lee, H., Park, S. J., 2007. MONERS: A news recommender for the mobile web. Expert Systems with Applications, 32(1):143 – 150.
(Lee et al. 2008) Lee, T. Q., Park, Y., Park, Y. T., 2008. A time-based approach to effective recommender systems using implicit feedback. ScienceDirect, Expert Systems with Applications, 34(4): 3055–3062.
(Lekakos & Caravelas, 2008) Lekakos, G., Caravelas, P., 2008. A hybrid approach for movie recommendation, Multimedia Tools and Applications, 36(1-2): 55-70
(Lin et al. 2006) Lin, C., Chou, S., Chen, C., et al., 2006. A final price prediction model for online English auctions-a Neuro fuzzy approach, In: Proceedings of Joint Conference on Information Sciences
(Lim et al. 2007) Lim, D., Anthony, P., Ho, C.M., 2007. Evaluating the Accuracy of Grey System Theory against Time Series in Predicting Online Auction Closing Price, In: Proceedings of IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China, 1: 463–470
(Lim et al. 2008a) Lim, D., Anthony, P., Ho, C.M., 2008. Agent for Predicting Online Auction Closing Price in a Simulated Auction Environment, In: Ho T-B and Zhou Z-H (eds.) PRICAI 2008: Trends and Artificial Intelligence. Springer, Heidelberg, 5351: 223–234
(Lim et al. 2008b) Lim, D., Anthony, P., Ho C.M., 2008. Predictor Agent for Online Auctions, In: Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications, Springer, Heidelberg, 4953: 381–390
(Liou & Liu, 2010) Liou, C-H., Liu, D-R., 2010. Hybrid recommendations for mobile commerce based on mobile phone features. Expert systems, DOI: 10.1111/j.1468-0394.2010.00566.x
(Liu et al. 2010) Liu, J., Dolan, P., Pedersen, E.R., 2010. Personalized news recommendation based on click behavior. In: Rich, et al. (eds.) Proc. of 14th Int. Conf. on Intelligent User Interfaces (IUI), pp. 31–40
(Liu & Liou, 2011) Liu, D-R., Liou, C-H., 2011. Mobile commerce product recommendations based on hybrid multiple channels. Electronic commerce Research and Applications 10:94-104
(Liu et al. 2002) Liu, H., Hussain, F., Tan, C.L., et al, 2002. Discretization: an enabling technique. Data Min Knowl Disc 6:393–423
(Lops et al. 2011) Lops, P., de Gemmis, M., Semeraro, G., 2011. Content-based Recommender Systems: State of the Art and Trends F. Ricci et al. (eds.), Recommender Systems Handbook
(Martinez-Gil & Aldana-Montes, 2011) Martinez-Gil, J., Aldana-Montes, J.F., 2011. Evaluation of two heuristic approaches to solve the ontology meta-matching problem. Knowl Inf Syst. 26(2): 225-247
(McAfee & McMillan, 1987) McAfee, R.P., McMillan, J., 1987. Auctions with a stochastic number of bidders. Journal of Economic Theory 43 (1): 1–19.
(Min & Han, 2005) Min, S-H., Han, I., 2005. Detection of the customer time-variant pattern for improving recommender systems, Expert System with Applications 28: 189-199
(Milgromn & Weber, 1982) Milgrom, P.R., Weber, R.J., 1982. A theory of auctions and competitive bidding. Econometrica, 50 (5): 1089–1122.
(Mooney & Roy, 2000) Mooney, R.J., Roy, L., 2000. Content-Based Book Recommending Using Learning for Text Categorization, In: Proceeding DL ''00 Proceedings of the fifth ACM conference on Digital libraries Workshop Recommender Systems: Algorithms and Evaluation
(Murthi & Sarkar, 2003) Murthi, B.P.S., Sarkar, S., 2003. The Role of the Management Sciences in Research on Personalization, Management Science, 49(10) :1344-1362,
(Nichols, 1997) Nichols, D.M., 1997. Implicit Rating and Filtering. In: Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, 31-36.
(Oard & Kim, 1998) Oard, D. W., Kim, J., 1998. Implicit Feedback for Recommender Systems. In: Proceedings of the AAAI Workshop on Recommender Systems, 81-83.
(Pazzani & Billsus, 2007) Pazzani, M.J., Billsus, D., 2007. Content-based Recommendation Systems, Adaptive Web, LNCS, Volume 4321/2007, 325-341
(Patnaik et al. 2010) Patnaik, D., Laxman, S., Ramakrishnanm, N., 2010. Discovering excitatory relationships using dynamic Bayesian networks. Knowl Inf Syst pp 1–31. doi:10.1007/s10115-010-0344-6
(Park et al. 2007) Park, M.-H., Hong, J.-H., Cho, S.-B., 2007. Location-based recommendation system using bayesian user’s preference model in mobile devices. In Indulska, J., Ma, J., Yang, L. T., Ungerer, T., and Cao, J., editors, UIC, 4611: 1130–1139. Springer.
(Powell, 1981) Powellm, M.J.D., 1981. Approximation Theory and Methods. Cambridge Univ. Press
(Peter et al. 1996) Peter, S., Fader and Bruce, Hardie G. S., 1996. Modeling Consumer Choice among SKUs, Journal of Marketing Research, 33(4): 442-452
(Phelan et al. 2011) Phelan, O., McCarthy, K., Bennett, M., et al., 2011. Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter, Advances in Information Retrieval, Lecture Notes in Computer Science, 6611:448-459, DOI: 10.1007/978-3-642-20161-5_44
(Qin et al. 2010) Qin, B., Xia, Y., Prabhakar, S., 2010. Rule induction for uncertain data. Knowl Inf Syst doi:10.1007/ s10115-010-0335-7
(Ricci et al. 2006) Ricci, F., Cavada, D., Mirzadeh, N., et al., 2006. Case-based travel recommendations. In Fesenmaier, D. R., Woeber, K., and Werthner, H., editors, Destination Recommendation Systems: Behavioural Foundations and Applications, pp 67–93
(Raykhel & Ventura, 2009) Raykhel, I., Ventura, D., 2009. Real-time automatic price prediction for eBay online trading, In: Proceedings of the Twenty-First Innovative Applications of Artificial Intelligence Conference, pp 135–140.
(Reingen & Kernan, 1993) Reingen, P.H., Kernan, J.B., 1993. Social perception and interpersonal influence: Some consequences of the physical attractiveness stereotype in a personal selling setting. Journal of Consumer Psychology 2 (1), 25–38.
(Reindorp et al. 2005) Reindorp, M., Jank, W., Raschid, L., 2005. The Right Auction at the Right Price. Robert H Smith School of Business, University of Maryland, Working paper No. RHS-06-009.
(Richards, 2000) Richards, B., 2000. Dear supplier: This is going to hurt you more than it hurts me… . Ecompany Now 1(1): 136–142
(Robbins, 2005) Robbins, S.P., 2005. Essentials of organizational behavior (7th ed.) Englewood Cliffs, NJ: Prentice Hall
(von Reischach et al. 2009) von Reischach, F., Guinard, D., Michahelles, F., et al., 2009. A mobile product recommendation system interacting with tagged products. In: Proceedings of the 7th International Conference on Pervasive Computing and Communications
(Sarwar et al. 2001) Sarwar, B., Karypis, G., et al., 2001. Item-based Collaborative Filtering recommendation Algorithms, In: Proceedings of the 10th international conference on World Wide Web, New York, USA
(Michael et al. 2006) Michael, S. L., Sebe, N., Djeraba, C., 2006. Content-based multimedia information retrieval: State of the art and challenges, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 2:1
(Stuhlmacher et al. 1998) Stuhlmacher, A.F., Gillespie, T.L., Champagne, M.V., 1998. The impact of time pressure in negotiation: A meta analysis. International Journal of Conflict Management 9 (2), 97–116.
(Tang et al. 2003) Tang, T. Y., Winoto, P., Chan, K. C. C., 2003. Scaling down candidate sets based on the temporal feature of items for improved hybrid recommendations. In: Intelligent techniques in web personalization Springer, LNAI 3169, pp. 169–185.
(Thompson & Hastie, 1990) Thompson, L., Hastie, R., 1990. Social perception in negotiation. Organizational Behavior and Human Decision Processes 47 (1): 98–123.
(Tran & Cohen, 2000) Tran, T., Cohen, R., 2000. Hybrid Recommender Systems for Electronic Commerce, In: Proceeding of Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS-00-04, AAAI Press
(Tumas & Ricci, 2009). Tumas, G., Ricci, F., 2009. Personalized mobile city transport advisory system. In Information and Communication Technologies in Tourism, pp: 173–184.
(Tully, 2000) Tully, S., 2000. Going, going, gone! The B2B tool that really is changing the world, Fortune 141(6): 132– 145
(van Heijst et al. 2008) van Heijst, D., Potharst, R., van Wezel, M., 2008. A support system for predicting eBay end prices, Decis Support Syst 44 (4): 970–982
(Wei, 2010) Wei, G.W., 2010. Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Knowl Inf Syst 25(3): 623-634
(Wang et al. 2008) Wang, S., Jank, W., Shmueli, G., 2008. Explaining and forecasting online auction prices and their dynamics using functional data analysis. J Bus Econ Stat 26(2): 144-160
(Wang & Yang, 2010) Wang, T., Yang, J., 2010. A heuristic method for learning Bayesian networks using discrete particle swarm optimization. Knowl Inf Syst 24(2): 269-281
(Xuefeng et al. 2006) Xuefeng, L., Lu L., Lihua, W., et al., 2006. Predicting the final prices of online auction items, Expert Syst Appl, 31(3): 542–550
(Yeung & Yang, 2010) Yeung, F., Yang, Y., 2010. A Proactive Personalized Mobile News Recommendation System Kam In: Proceeding Developments in E-systems Engineering, London
(Zhang & Liu, 2010) Zhang, Y., Liu, Y., 2010. A Collaborative Filtering Algorithm based on Time Period Partition, In: Proceeding Third International Symposium on Intelligent Information Technology and Security Informatics, Beijing, China, pp: 777 – 780
(OECD, 2009) The Organization for Economic Development Background Report. 2009. In: OECD Conference on Empowering E-consumers, Washington D.C. http://www.oecd.org/dataoecd/ 44/13/44047583.pdf
(SEC, 2009) United States Securities and Exchange Commission, 2009. Form 10-K (eBay Inc:), Washington, 20549
http://investor.ebay.com/secfiling.cfm?filingID=950134-09-3306
(Amazon.com, Inc, 2010) The report of financial result of second quarter of Amazon.com, Inc.,
http://phx.corporate-ir.net/phoenix.zhtml?c=97664&p=irol-newsArticle&ID=1451041&highlight=
(eBay Inc, 2011) eBay Inc. Reports Strong Third Quarter 2011 Results, 2011.
http://investor.ebayinc.com/releasedetail.cfm?ReleaseID=616406
(ABI 2010) ABI research, 2010.
https://www.abiresearch.com/press/1605-Shopping+by+Mobile+Will+Grow+to+$119+Billion+in+2015