E-Commerce, and Web Technologies

This book constitutes the refereed proceedings of the 14th International Conference on Electronic Commerce and Web Technologies (EC-Web) held in Prague, Czech Republic, in August 2013. In 2013, EC-Web focused on recommender systems, semantic e-business, business services and process management, and agent-based e-commerce. The 13 full and 6 short papers accepted for EC-Web, selected from 43 submissions, were carefully reviewed based on their originality, quality, relevance, and presentation.

Proceedings of the 14th International Conference E-Commerce, and Web Technologies

Inhalt
Session 1: Opening.- BRF: A Framework of Retrieving Brand Names of Products in Auction Sites.- An Adaptive Social Influence Propagation Model Based on Local Network Topology.- Session 2: Semantics and Agents.- A Rule Based Personalized Location Information System for the Semantic Web.- DIESECT: A Distributed Environment for Simulating E-commerce Contracts.- Semi-automated Structural Adaptation of Advanced E-Commerce Ontologies.- Session 3: Business Processes.- SDRule-L: Managing Semantically Rich Business Decision Processes.- A Hybrid Approach for Business Environment-Aware Management of Service-Based Business Processes.- Discovering Workflow-Aware Virtual Knowledge Flows for Knowledge Dissemination.- Session 4: Recommender I.- An Emotion Dimensional Model Based on Social Tags: Crossing Folksonomies and Enhancing Recommendations.- Cold-Start Management with Cross-Domain Collaborative Filtering and Tags.- UtilSim: Iteratively Helping Users Discover Their Preferences.- Session 5: Recommender II.- Contextual eVSM: A Content-Based Context-Aware Recommendation Framework Based on Distributional Semantics.- Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches.- Matching Ads in a Collaborative Advertising System.- Session 6: Recommender III.- Confidence on Collaborative Filtering and Trust-Based Recommendations.- Smoothly Extending e-Tourism Services with Personalized Recommendations: A Case Study.- Exploiting Big Data for Enhanced Representations in Content-Based Recommender Systems.- Recommendations Based on Different Aspects of Influences in Social Media.- Robustness Analysis of Naive Bayesian Classifier-Based Collaborative Filtering.