Complete list of publications

  1. SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication.

    Shaden Smith, Niranjay Ravindran, Nicholas D. Sidiropoulos, and George Karypis. 29th IEEE International Parallel & Distributed Processing Symposium, 2015.

  1. Mining Coevolving Induced Relational Motifs in Dynamic Networks.

    Rezwan Ahmed and George Karypis. Workshop on Dynamic Networks (SDM-Networks), SIAM Data mining Conference, 2015.

  1. Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation.

    Mohit Sharma, Jiayu Zhou, Junling Hu, and George Karypis. 2015 SIAM International Conference on Data Mining, 2015.

  1. A comprehensive survey of neighborhood-based recommendation methods.

    Xia Ning, Christian Desrosiers, and George Karypis. In Recommender Systems Handbook; 2nd edition, 2015.

  1. Understanding Computer Usage Evolution.

    David C. Anastasiu, Al M. Rashid, Andrea Tagarelli, and George Karypis. 31st IEEE International Conference on Data Engineering (ICDE), 2015.

  1. NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems.

    Santosh Kabbur and George Karypis. 7th ICDM International Workshop on Domain Driven Data Mining (DDDM), 2014.

  1. Memory-Efficient Parallel Computation of Tensor and Matrix Products for Big Tensor Decomposition.

    Niranjay Ravindran, Nicholas D. Sidiropoulos, Shaden Smith, and George Karypis. 28th Asilomar Conference on Signals, 2014.

  1. HOSLIM: Higher-Order Sparse Linear Method for Top-N Recommender Systems.

    Evangelia Christakopoulou and George Karypis. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2014.

  1. Algorithms for Mining the Coevolving Relational Motifs in Dynamic Networks.

    Rezwan Ahmed and George Karypis. UMN CS 14-008, 2014.

  1. Signaling Adverse Drug Reactions with Novel Feature-based Similarity Model.

    Fan Yang, Xiaohui Yu, and George Karypis. IEEE Conf. on Bioinformatics and Biomedicine (BIBM), 2014.

  1. Personalized Multi-Regression Models for Predicting Students' Performance in Course Activities.

    Asmaa Elbadrawy, R. Scott Studham, and George Karypis. UMN CS 14-011, 2014.

  1. User-Specific Feature-based Similarity Models for Top-N Recommendation of New Items.

    Asmaa Elbadrawy and George Karypis. UMN CS 14-016, 2014.