Thu, 21 Aug 2008 01:17:07 BST CiteULike: imrchen's e-commerce http://www.citeulike.org/user/imrchen/tag/e-commerce CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/imrchen/article/2951474 <i>Knowledge Discovery and Data Mining, 2008. WKDD 2008. International Workshop on (2008), pp. 406-409.</i><br /><br />In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers' needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using k-means algorithm and data from Web log of various e-commerce Websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior. Mining E-Commerce Data to Analyze the Target Customer Behavior Yuantao Jiang Siqin Yu doi:10.1109/WKDD.2008.90 Knowledge Discovery and Data Mining, 2008. WKDD 2008. International Workshop on (2008), pp. 406-409. 2008-07-02T08:06:14-00:00 2008 Knowledge Discovery and Data Mining, 2008. WKDD 2008. International Workshop on 406 409 e-commerce http://www.citeulike.org/user/imrchen/article/2900151 <i>(2007), pp. 56-65.</i> Show me the money!: deriving the pricing power of product features by mining consumer reviews Nikolay Archak Anindya Ghose Panagiotis Ipeirotis doi:10.1145/1281192.1281202 (2007), pp. 56-65. 2008-06-16T22:54:09-00:00 2007 56 65 ACM e-commerce http://www.citeulike.org/user/imrchen/article/2949995 <i>Statistical Science, Vol. 21 (2006)</i> Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation Stephen Fienberg Statistical Science, Vol. 21 (2006) 2008-07-02T06:19:20-00:00 2006 Statistical Science 21 143 e-commerce http://www.citeulike.org/user/imrchen/article/2949889 <i>Expert Systems with Applications, Vol. 28, No. 2. (February 2005), pp. 381-393.</i><br /><br />In this article, a novel CF (collaborative filtering)-based recommender system is developed for e-commerce sites. Unlike the conventional approach in which only binary purchase data are used, the proposed approach analyzes the data captured from the navigational and behavioral patterns of customers, estimates the preference levels of a customer for the products which are clicked but not purchased, and CF is conducted using the preference levels for making recommendations. This also compares with the existing works on clickstream data analysis in which the navigational and behavioral patterns of customers are analyzed for simple relationships with the target variable. The effectiveness of the proposed approach is assessed using an experimental e-commerce site. It is found among other things that the proposed approach outperforms the conventional approach in almost all cases considered. The proposed approach is versatile and can be applied to a variety of e-commerce sites as long as the navigational and behavioral patterns of customers can be captured. Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites Yong Kim Bong-Jin Yum Junehwa Song Su Kim doi:10.1016/j.eswa.2004.10.017 Expert Systems with Applications, Vol. 28, No. 2. (February 2005), pp. 381-393. 2008-07-02T04:17:51-00:00 2005 Expert Systems with Applications 28 2 381 393 e-commerce recommender http://www.citeulike.org/user/imrchen/article/2949863 <i>International Journal of Industrial Organization, Vol. 25, No. 3. (June 2007), pp. 541-574.</i><br /><br />This paper analyzes retailers' adoption of e-commerce in a technology adoption race framework. An internet-based firm with no traditional market presence competes with an established traditional firm to adopt the e-commerce technology and sell to a growing number of consumers with on-line shopping capability. The focus of the analysis is on identifying how consumer loyalty, differences in firms' technology and consumers' preferences for the traditional versus the virtual market, and the expansion in market size made possible by the internet can affect the timing and sequence of adoption by firms, as well as the post-adoption evolution of prices. The model's implications are used to discuss empirical evidence on adoption patterns for different product categories and firm types. On the diffusion of electronic commerce Emin Dinlersoz Pedro Pereira doi:10.1016/j.ijindorg.2006.05.008 International Journal of Industrial Organization, Vol. 25, No. 3. (June 2007), pp. 541-574. 2008-07-02T03:54:58-00:00 2007 International Journal of Industrial Organization 25 3 541 574 e-commerce http://www.citeulike.org/user/imrchen/article/2824070 <i>European Journal of Operational Research, Vol. 187, No. 3. (16 June 2008), pp. 671-690.</i><br /><br />The Internet has provided traditional retailers a new means with which to serve customers. Consequently, many "bricks-and-mortar" retailers have transformed to "clicks-and-mortar" by incorporating Internet sales. Examples of companies making such a transition include Best Buy, Wal-Mart, Barnes & Noble, etc. Despite the increasing prevalence of this practice, several fundamental questions remain: (1) Does it pay off to go online? (2) Which is the equilibrium industry structure? (3) What is the implication of this business model for consumers? We study these issues in an oligopoly setting and show that clicks-and-mortar arises as the equilibrium channel structure. However, we find that this equilibrium does not necessarily imply higher profits for the firms: in some cases, rather, it emerges as a strategic necessity. Consumers are generally better off with clicks-and-mortar retailers. If firms align with pure e-tailers to reach the online market, we show that a prisoner's dilemma-type equilibrium may arise. "Bricks-and-mortar" vs. "clicks-and-mortar": An equilibrium analysis Fernando Bernstein Jing-Sheng Song Xiaona Zheng doi:10.1016/j.ejor.2006.04.047 European Journal of Operational Research, Vol. 187, No. 3. (16 June 2008), pp. 671-690. 2008-05-22T22:09:25-00:00 2008 European Journal of Operational Research 187 3 671 690 e-commerce http://www.citeulike.org/user/imrchen/article/2949750 <i>Machine Learning and Cybernetics, 2007 International Conference on, Vol. 7 (2007), pp. 4113-4118.</i><br /><br />Due to the convenience of Internet, people can search for whatever information they need and buy whatever they want on the web. In the age of E-Commerce, it is difficult to provide support for customers to find the most valuable products that match their heterogeneous needs. Traditional approaches to this so-called personalization problem adopt predefined formats to describe the customer requirements. This always leads to distortion in eliciting requirement information and thus inaccurate recommendations. In this paper, we propose a personalized recommendation system using association rule mining and classification in e-commerce. Customer requirements are extracted from text documents and transformed into a set of significant phrases. Allowing the transformed transaction records, a set of association rule are mined from database using Apriori algorithm. CBA-CB algorithm is applied to produce the best rules out of the whole set of rules. The best classifiers are then generated after the test and validation of those rules, aimed to predict the item labels for new customer requirements and thus assigns the corresponding class labels to the customer. The system analysis and design of the proposed recommendation system as well as the implementation of prototype are also presented. Building Personalized Recommendation System in E-Commerce using Association Rule-Based Mining and Classification Xi-Zheng Zhang doi:10.1109/ICMLC.2007.4370866 Machine Learning and Cybernetics, 2007 International Conference on, Vol. 7 (2007), pp. 4113-4118. 2008-07-02T02:33:27-00:00 2007 Machine Learning and Cybernetics, 2007 International Conference on 7 4113 4118 e-commerce recommender http://www.citeulike.org/user/imrchen/article/2948615 <i>Technovation, Vol. 27, No. 1-2. ( 2007), pp. 47-56.</i><br /><br />The industrial revolution was to manufacturers what the digital revolution is to consumers. What we are seeing today is a renegotiation of the relationships between companies and consumers, and a fundamental recasting of conventional marketing in favor of the consumer. This study, therefore, discusses consumer power in marketing theory and analyzes consumer power sources and changing power dynamics with case studies. Finally, it contributes to theory by investigating power dynamics in each stage of the consumer decision-making process. An analysis of consumer power on the Internet Umit Sandeep Krishnamurthy doi:10.1016/j.technovation.2006.05.002 Technovation, Vol. 27, No. 1-2. ( 2007), pp. 47-56. 2008-07-01T15:19:40-00:00 2007 Technovation 27 1-2 47 56 e-commerce http://www.citeulike.org/user/imrchen/article/2943264 <i>Internet Computing, IEEE, Vol. 12, No. 1. (2008), pp. 60-65.</i><br /><br />E-commerce is a big business with a growing market size and has been a major driving force in the IT industry for the past decade. Companies now need to provide online shopping or marketing Web presence to allow for direct customer connections. In this article, the author reviews some primary e-commerce technologies, including auctions, negotiation, recommender systems, automated shopping, and trading. This paper also looks at how Web 2.0 provides new e-commerce opportunities. E-Commerce Technology: Back to a Prominent Future Kwei-Jay Lin doi:10.1109/MIC.2008.10 Internet Computing, IEEE, Vol. 12, No. 1. (2008), pp. 60-65. 2008-06-30T07:32:29-00:00 2008 Internet Computing, IEEE 12 1 60 65 e-commerce http://www.citeulike.org/user/imrchen/article/2943258 <i>Service Systems and Service Management, 2006 International Conference on, Vol. 2 (2006), pp. 1582-1586.</i><br /><br />Collaborative filtering recommender systems have become important tools of making personalized recommendations for products or services during a live interaction nowadays. However, there are still some drawbacks and challenges for CF based recommender system such as prediction accuracy, scalability and sparsity. This paper points out that from a certain angle, the predictions these systems produce are not really personalized ones which lead to the above problems. After the analysis of the traditional collaborative filtering algorithm, the authors then proposes a new personalized recommender algorithm based on traditional CF algorithm to improve the recommender system. At last the effectiveness and superiority of the proposed novel algorithm is proved by four experiments using both cosine correlation similarity and Pearson correlation similarity in this paper An Improved Personalized Collaborative Filterinng Algolrithm in E-Commerce Recommender System Yanhong Guo Guishi Deng doi:10.1109/ICSSSM.2006.320772 Service Systems and Service Management, 2006 International Conference on, Vol. 2 (2006), pp. 1582-1586. 2008-06-30T07:28:53-00:00 2006 Service Systems and Service Management, 2006 International Conference on 2 1582 1586 e-commerce recommender http://www.citeulike.org/user/imrchen/article/2943249 <i>Service Systems and Service Management, 2007 International Conference on (2007), pp. 1-5.</i><br /><br />Various personal services in business play important roles in the success of current marketing field. The personalized recommendation technique in recommender systems, one of the most important tools of personal service in websites, makes great significance in Internet marketing activities of e-Commerce. Through summarizing and analyzing personalized recommendation research, this paper presents an overview of personalized recommendation technique and proposes future research topics. The research content of this paper mainly includes the following three aspects, (1) the input of recommender systems, such as the acquisition and presentation of customers' interest profile as well as items profiles; (2) the typical methods of various recommendation techniques; and (3) based on current research and application situations, we finally discuss the future research hot topics and give some suggestions for the research on future recommendation technique. A Survey of E-Commerce Recommender Systems Kangning Wei Jinghua Huang Shaohong Fu doi:10.1109/ICSSSM.2007.4280214 Service Systems and Service Management, 2007 International Conference on (2007), pp. 1-5. 2008-06-30T07:17:08-00:00 2007 Service Systems and Service Management, 2007 International Conference on 1 5 e-commerce recommender http://www.citeulike.org/user/imrchen/article/2927924 <i>(6 May 2008)</i><br /><br />Sponsored search involves running an auction among advertisers who bid in order to have their ad shown next to search results for specific keywords. Currently, the most popular auction for sponsored search is the "Generalized Second Price" (GSP) auction in which advertisers are assigned to slots in the decreasing order of their "score," which is defined as the product of their bid and click-through rate. In the past few years, there has been significant research on the game-theoretic issues that arise in an advertiser's interaction with the mechanism as well as possible redesigns of the mechanism, but this ranking order has remained standard. From a search engine's perspective, the fundamental question is: what is the best assignment of advertisers to slots? Here "best" could mean "maximizing user satisfaction," "most efficient," "revenue-maximizing," "simplest to interact with," or a combination of these. To answer this question we need to understand the behavior of a search engine user when she sees the displayed ads, since that defines the commodity the advertisers are bidding on, and its value. Most prior work has assumed that the probability of a user clicking on an ad is independent of the other ads shown on the page. We propose a simple Markovian user model that does not make this assumption. We then present an algorithm to determine the most efficient assignment under this model, which turns out to be different than that of GSP. A truthful auction then follows from an application of the Vickrey-Clarke-Groves (VCG) mechanism. Further, we show that our assignment has many of the desirable properties of GSP that makes bidding intuitive. At the technical core of our result are a number of insights about the structure of the optimal assignment. Sponsored Search Auctions with Markovian Users Gagan Aggarwal Jon Feldman S Muthukrishnan Martin Pal (6 May 2008) 2008-06-26T02:31:04-00:00 2008 e-commerce search_engine http://www.citeulike.org/user/imrchen/article/2925283 <i>(2001), pp. 8-13.</i> Mining e-commerce data: the good, the bad, and the ugly Ron Kohavi doi:10.1145/502512.502518 (2001), pp. 8-13. 2008-06-25T07:24:54-00:00 2001 8 13 ACM data-mining e-commerce http://www.citeulike.org/user/imrchen/article/2922925 <i>(2005), pp. 627-632.</i> Application of e-commerce security management strategy in banking Guoling Lao Liping Wang doi:10.1145/1089551.1089664 (2005), pp. 627-632. 2008-06-24T09:24:12-00:00 2005 627 632 ACM e-commerce security http://www.citeulike.org/user/imrchen/article/2652713 <i>Data Min. Knowl. Discov., Vol. 5, No. 1-2. (2001), pp. 5-10.</i> Applications of Data Mining to Electronic Commerce Ron Kohavi Foster Provost Data Min. Knowl. Discov., Vol. 5, No. 1-2. (2001), pp. 5-10. 2008-04-11T07:16:29-00:00 2001 Data Min. Knowl. Discov. 1384-5810 5 1-2 5 10 Kluwer Academic Publishers data-mining e-commerce http://www.citeulike.org/user/imrchen/article/2634713 <i>Manage. Sci., Vol. 50, No. 3. (March 2004), pp. 326-335.</i> Dynamic Conversion Behavior at E-Commerce Sites Wendy Moe Peter Fader doi:10.1287/mnsc.1040.0153 Manage. Sci., Vol. 50, No. 3. (March 2004), pp. 326-335. 2008-04-06T13:56:52-00:00 2004 Manage. Sci. 0025-1909 50 3 326 335 INFORMS data-mining e-commerce http://www.citeulike.org/user/imrchen/article/25744 <i>Machine Learning, Vol. 57, No. 1-2., 83.</i> Lessons and Challenges from Mining Retail E-Commerce Data: Special Issue: Data Mining Lessons Learned (Guest Editors: Nada Lavrac, Hiroshi Motoda and Tom Fawcett) Ron Kohavi Llew Mason Rajesh Parekh Zijian Zheng doi:10.1023/B:MACH.0000035473.11134.83 Machine Learning, Vol. 57, No. 1-2., 83. 2004-12-28T16:30:27-00:00 Machine Learning 0885-6125 57 1-2 83 Kluwer Academic Publishers data-mining e-commerce