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SIGBPS AOI: Business Intelligence in E-Commerce Track Chairs:
Qiang Wei (Tsinghua University)
Track Description: The advent of E-Commerce and its ubiquitous penetration to our daily life have reshaped the business world drastically. Business intelligence, turning the sheer volume of data gathered from E-Commerce processes into actionable business insights, has become a critical factor for the success of E-Commerce. In recent years, with the explosion of social media and its close interaction with E-Commerce, business intelligence has been further adapted to handle the significant challenges from the so-called big data. This track aims to bring together people from both academia and industry to present their most recent work related to business intelligence and E-Commerce, and exchange ideas and thoughts in order to advance this exciting joint area. We invites high-quality, original papers describing applications of data mining and knowledge discovery to E-Commerce, as well as applied and fundamental research addressing data mining issues particular to these areas. Problems of interest include, but not limited to: recommender systems, social media computing and social network analysis, sentiment discovery and opinion mining, online advertising and internet economy, mobile computing and location-based services, big data analytics and cloud computing, integration of E-Commerce systems and data warehousing, customer segmentation and profiling, fraud detection and network security, privacy-preserving data mining, clickstream mining, text mining, and BI cases in E-Commerce.
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