Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Multiagent data collection in Lycos
Communications of the ACM
SPHINX: a framework for creating personal, site-specific Web crawlers
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Electronic commerce: a managerial perspective
Electronic commerce: a managerial perspective
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
ATTac-2000: an adaptive autonomous bidding agent
Proceedings of the fifth international conference on Autonomous agents
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Nomad: Mobile Agent System for an Internet-Based Auction House
IEEE Internet Computing
CI Spider: a tool for competitive intelligence on the web
Decision Support Systems
Web page clustering using a self-organizing map of user navigation patterns
Decision Support Systems - Special issue: Web data mining
C2C Versus B2C: A Comparison of the Winner's Curse in Two Types of Electronic Auctions
International Journal of Electronic Commerce
Price prediction in a trading agent competition
Journal of Artificial Intelligence Research
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
An ontological website models-supported search agent for web services
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Since the emergence of online auctions in 1995, many individuals have joined auction markets. Sometimes, many bidders have made wrong decisions (e.g., ''Winner Curse'') due to their limited knowledge and resources. Unfortunately, search engines only show a list of search results to users, and fail to provide further analysis that could help improve users' decision-making. To solve this problem, this study proposed an intelligent spider for information retrieval, and applied data mining technology to differentiate between customers. Two software programs, a URL searching agent and an auction data agent, are developed to automatically collect related information whenever users input the searched product. Two neural networks are used to perform data clustering and price prediction after this information is crawled and stored into a database. The first neural network adopts a self-organizing map (SOM) to cluster customer data into nine homogenous groups. The second backpropagation network (BPN) is then used to predict the final price. This study develops a prototype of the proposed spider, and conducts an empirical study by crawling over 1000 deals from Taiwan's eBay. Finally, important information, such as predicted price, prediction error and historic records, are presented to the user. The user can thus easily target the right bidding policy for wining a bid based on the mining-based information about price prediction.