There has been an increasing shift away from traditions
of individual based scientific research toward more collaborative models via
online scientific communities. One famous example of scientific online
communities is nanoHUB.org powered by the HUBzero platform. nanoHUB has been
well received by nanotechnology community and has attracted more than 90,000
active users by providing thousands of resources such as simulation tools,
teaching materials and publications. The rapid growth of information in
scientific online communities demands intelligent agents that can identify the
most valuable to the users. Existing solutions of information recommendation are
not adequate for online scientific communities. For example, users in online
scientific communities undertake different types of tasks (e.g., seeking
teaching materials or conducting experiments for dissertation work) and require
recommendation that distinguishes different tasks, which is not provided by
existing recommendation solutions. Furthermore, a substantial amount of
information from users of online scientific communities is implicit feedback
(e.g., click through data). However, most existing recommendation solutions
focus on explicit feedback information (e.g., user ratings of movies).
The proposed research seeks to overcome the limitations of existing recommendation solutions with a new integrated information recommendation framework for online scientific communities. The proposed research thrusts include: (1) Task-Specific Recommendation: estimate possible tasks undertaken and incorporate the estimation results into the process of making recommendation; (2) Intelligent Hybrid Recommendation: integrate collaborative recommendation and content-based recommendation techniques within a single model that intelligently tunes the weights of content based information and collaborative usage information; (3) Pairwise Comparison Approach for Implicit Feedback: model users? implicit feedback information of recommended resources in a probabilistic model with a natural assumption of pairwise comparison; (4) System Development and Evaluation: integrate proposed algorithms into the HUBzero platform. The research results will be evaluated in carefully designed user studies as well as in real world operational environments (i.e., nanoHUB).
The proposed research will yield substantial benefits in broad areas. The information recommendation tool will be incorporated into nanoHUB to benefit a large number of users. The source code of proposed algorithms will be released with the HUBzero platform to enable further advance and development in information recommendation. The proposed information recommendation solutions can be adapted and used in other general purpose social network applications like LinkedIn/Facebook. Some research topics will be integrated into the courses that the PIs teach. The PIs will encourage the involvement of underrepresented students in the research project.
Last modified: Friday, June 10, 2011 01:49 PM