Information Recommendation for Online Scientific Communities

Research Areas: Human Centric Security

Principal Investigator: Luo Si

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 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.


Other PIs: PI: Luo Si co-PI: Gerhard Klimeck (Co-Principal Investigator); Michael McLennan (Co-Principal Investigator)

Representative Publications

  • Yi Fang, Luo Si, Naveen Somasundaram, Zhengtao Yu. (2012) "Mining Contrastive Opinions on Political Texts using Cross-Perspective Topic Model". In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM)

    Yi Fang and Luo Si. (2011) "Matrix Co-Factorization for Recommendation with Rich Side Information and Implicit Feedback". In Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems.

    Suleyman Cetintas, Datong Chen and Luo Si. (2012). “Forecasting User Visits for Online Display Advertising.”  (Journal of Information Retrieval).

    Dan Zhang, Jingdong Wang, Luo Si. (2011). “Document Clustering with Universum”. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

    Dan Zhang, Fei Wang, Luo Si. (2011). “Composite Hashing with Multiple Information Sources”. International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

    Yi Fang, Luo Si, Zhengtao Yu, Naveen Somasundaram and Yantuan Xian. Purdue at TREC 2010 Entity Track: a Probabilistic Framework for Matching Types between Candidate and Target Entities. In Proceedings of the 18th Text REtrieval Conference (TREC), Gaithersburg, USA, 2010.Rong Jin, Luo Si, ChengXiang Zhai. (2006). "A Study of Mixture Models for Collaborative Filtering" Journal of Information Retrieval.

    Luo Si and Rong Jin. (2003). "Flexible Mixture Model for Collaborative Filtering" In Proceedings of the Twentieth International Conference on Machine Learning (ICML).

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Our annual security symposium will take place on April 7th and 8th, 2020.
Purdue University, West Lafayette, IN

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