With the explosive growth of resources available through the Internet, information overload has become a serious issue. Especially the emergence of social media has created highly interactive platforms for users to create, share, exchange information and build social networks. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant information. Recommender systems represent tools for efficient selection of the most relevant information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from technology perspective and social perspective; also solutions are needed for effective interaction & collaboration between users and maintain trustworthiness and reliability of information on social media. The aim of this workshop is to promote high quality research in technical and human aspects related to Web personalization, social media and resource selection through recommender systems. The workshop will provide a forum for academic and industrial researchers to exchange ideas about past, present and future trends in Web personalization, social media and resource selection, and for discussing new and innovative approaches.
Topics of Interest
Topics include but are not limited to:
- User behaviour modelling and personalization techniques
- Collaborative and content based filtering
- Clustering and classification in recommender systems
- Hybrid recommender systems
- Security and trust in recommender systems
- Trust and reputation management
- Ontology learning and semantic web technologies
- Content management and modelling
- Product modelling, user opinion mining and data extraction
- Adaptive user interfaces
- Recommender applications for social media sites
- Explanation and justification in recommender systems
- Distributed and peer-to-peer recommender systems
- Modelling decision making in e-commerce systems
- Measuring personalization effectiveness
- Evaluation methods for recommender systems
- Ownership of social media content
- Security and Privacy in social media
- Trustworthiness and reliability on social media
Paper submissions should be limited to 6 – 8 pages for regular papers or 4 pages for short papers in ACM 2-column format. The style files for paper submission can be obtained from the WI2017 site or http://webintelligence2017.com/participants/submissions/#submission.
All submitted papers will be reviewed by at least 2 program committee members on the basis of technical quality, relevance, significance, and clarity. Accepted papers will be published in the conference workshop proceedings by ACM and indexed by EI.
The workshop only accepts on–line submissions. Workshops on–line submission page can be accessed through the WI 2017 Submission site.
Yue Xu, Queensland University of Technology, Australia, yue.xu [at] qut.edu [dot] au
Gabriella Pasi, University of Milano Bicocca, Italy pasi [at] disco.unimib [dot] it
Yuefeng Li, Queensland University of Technology, Australia, y2.li [at] qut.edu [dot] au
Yong Zheng, Illinois Institute of Technology, USA, yzheng66 [at] iit [dot] edu