The Recommender Systems 2011 Doctoral Symposium provided an opportunity for doctoral students to explore and develop their research interests in an interdisciplinary workshop, under the guidance of a panel of distinguished research faculty. We invited students who feel they would benefit from this kind of feedback on their dissertation work to apply for this unique opportunity to share their work with students in a similar situation as well as senior researchers in the field. The strongest candidates were those who had an idea and an area, and had made some progress, but who were not so far along that they can no longer make changes. Typically, this means they had made their dissertation proposal, but still were about a year from completion.
- Provide a supportive setting for feedback on students’ current research and guidance on future research directions.
- Offer each student comments and fresh perspectives on their work from faculty and students outside their own institution.
- Promote the development of a supportive community of scholars and a spirit of collaborative research.
- Contribute to the conference goals through interaction with other researchers and conference events.
The symposium has been held on October 23rd (Sunday). About 5-8 doctoral students and four established researchers have been invited to participate. Student participants had their abstracts published in the conference proceedings and exhibited a poster of their work at the main conference. All participants were expected to attend the entire symposium. Each student presented his or her work to the group with substantial time allowed for discussion and questions by participating faculty and other students. Being accepted into the symposium is an honor, and involves a commitment to giving and receiving thoughtful commentary with an eye towards shaping the field and upcoming participants in the field.
- Siamak Faridani: Using Canonical Correlation Analysis for Generalized Sentiment Analysis, Product Recommendation and Search
- Markus Tschersich: Design Guidelines for Mobile Group Recommender Systems to Handle Inaccurate or Missing Location Data
- Yu Chen: Interface and Interaction Design for Group and Social Recommender Systems
- Shafiq Alam: Intelligent Web Usage Clustering Based Recommender System
- Alejandro Bellogín: Predicting Performance in Recommender Systems
- Jingjing Zhang: Anchoring Effects of Recommender Systems
- Alexander Tuzhilin, New York University, United States
- Domonkos Tikk, Gravity R&D/Budapest University of Technology and Economics, Hungary