Publications Related to Collaborative filtering

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

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

  1. MPI for Big Data: New Tricks for an Old Dog.

    Dominique LaSalle and George Karypis. Parallel Computing, 2014.

  1. A Versatile Graph-based Approach to Package Recommentation.

    Roberto Interdonato, Salvatore Romeo, Andrea Tagarelli, and George Karypis. IEEE International Conference on Tools with Artificial Intelligence, 2013.

  1. FISM: Factored Item Similarity Models for Top-N Recommender Systems.

    Santosh Kabbur, Xia Ning, and George Karypis. 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.

  1. Sparse Linear Methods with Side Information for Top-N Recommendations.

    Xia Ning and George Karypis. 6th ACM Recommender Systems Conference (RecSys), 2012.

  1. SLIM: Sparse Linear Methods for Top-N Recommender Systems.

    Xia Ning and George Karypis. ICDM , 2011.

  1. A Comprehensive Survey of Neighborhood-based Recommendation Methods.

    Christian Desrosiers and George Karypis. Recommender Systems Handbook, pp. 107-144, 2011.

  1. Multi-task Learning for Recommender Systems.

    Xia Ning and George Karypis. 2nd Asian Conference on Machine Learning (ACML), 2010.

  1. A Novel Approach to Compute Similarities and its Application to Item Recommendation.

    Christian Desrosiers and George Karypis. 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI), pp. 39—51, 2010.

  1. Learning Preferences of New Users in Recommender Systems: An Information Theoretic Approach.

    Al Mamunnur Rashid, George Karypis, and John Riedl. SIGKDD Workshop on Web Mining and Web Usage Analysis (WEBKDD), 2008.

  1. Towards a Scalable kNN CF Algorithm: Exploring Effective Applications of clustering.

    Al Mamunur Rashid, Shyong K. Lam, Adam LaPitz, George Karypis, and John Riedl. In “Web Mining and Web Usage Analysis”, O. Nasraoui, et. al. (editors), Springer, 2008.