Human Flesh Search Model Incorporating Network Expansion and GOSSIP with Feedback
DS-RT '09 Proceedings of the 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications
Agent-Based Computational Modeling of Emergent Collective Intelligence
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Identity, profiling algorithms and a world of ambient intelligence
Ethics and Information Technology
Improving search in tag-based systems with automatically extracted keywords
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Knowledge Aggregation in Human Flesh Search
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
A framework for semantic recommendations in situational applications
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Combining collaborative filtering and sentiment classification for improved movie recommendations
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Collective Intelligence in Organizations: Tools and Studies
Computer Supported Cooperative Work
The dicode workbench: a flexible framework for the integration of information and web services
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Hi-index | 0.00 |
There's a great deal of wisdom in a crowd, but how do you listen to a thousand people talking at once? Identifying the wants, needs, and knowledge of internet users can be like listening to a mob. In the Web 2.0 era, leveraging the collective power of user contributions, interactions, and feedback is the key to market dominance. A new category of powerful programming techniques lets you discover the patterns, inter-relationships, and individual profiles-the collective intelligence--locked in the data people leave behind as they surf websites, post blogs, and interact with other users. Collective Intelligence in Action is a hands-on guidebook for implementing collective intelligence concepts using Java. It is the first Java-based book to emphasize the underlying algorithms and technical implementation of vital data gathering and mining techniques like analyzing trends, discovering relationships, and making predictions. It provides a pragmatic approach to personalization by combining content-based analysis with collaborative approaches. This book is for Java developers implementing Collective Intelligence in real, high-use applications. Following a running example in which you harvest and use information from blogs, you learn to develop software that you can embed in your own applications. The code examples are immediately reusable and give the Java developer a working collective intelligence toolkit. Along the way, you work with, a number of APIs and open-source toolkits including text analysis and search using Lucene, web-crawling using Nutch, and applying machine learning algorithms using WEKA and the Java Data Mining (JDM) standard.