Call for Papers
With the explosive growth of resources available through the Internet, information overload has become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Recommender and reputation systems represent tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years, driven by the demand for online personalization applications. Web-based recommender systems are the most notable application of web personalization. Combined with reputation systems, Web users can get recommendations with high reliability and quality. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender and reputation systems from technology perspective and social perspective. Aiming to promote high quality research in order to overcome these challenges, this workshop will provide a forum for academic and industrial practitioners to exchange understandings of past, present and future trends in Web personalization and recommendation making, and strengthen the research in the areas of Web personalization, reputation and recommender systems.
Topics and areas include, but not limited to:
- User behaviour modelling
- Collaborative and content based filtering
- Clustering in recommender systems
- Hybrid recommender systems
- Security and trust in recommender systems
- Adaptive user interfaces and personalization techniques
- Recommender systems in negotiation and auctions
- Explanation and justification in recommender systems
- Distributed and peer–to–peer recommender systems
- Reputation systems
- Trust models
- Trust and reputation management
- Recommender applications for social media sites
- Social awareness and visualization
- Personalized ontology learning and mining
- Semantic Web Technologies
- Measuring personalization effectiveness
- Evaluation methods for recommender systems
- Opinion mining for user behaviour models