REQUEST: A Query Language for Customizing Recommendations

Published Online:https://doi.org/10.1287/isre.1100.0274

References

  • Adomavicius G., Tuzhilin A., Fiege L., Mühl G., Wilhelm U. Multidimensional recommender systems: A data warehousing approach. Electronic Commerce: Second Internat. Workshop (WELCOM 2001) (2001) 2232(Springer-Verlag, Heidelberg, Germany) 180–192Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Adomavicius G., Tuzhilin A. Incorporating context into recommender systems using multidimensional rating estimation methods. Proc. 1st Internat. Workshop Web Personalization, Recommender Systems and Intelligent User Interfaces (WPRSIUI 2005) (2005a) Reading, UK:3–13Google Scholar
  • Adomavicius G., Tuzhilin A. Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. (2005b) 17(6):734–749CrossrefGoogle Scholar
  • Adomavicius G., Sankaranarayanan R., Sen S., Tuzhilin A. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Systems (2005) 23(1):103–145CrossrefGoogle Scholar
  • Agrawal R., Gupta A., Sarawagi S. Modeling multidimensional databases. Proc. 13th Internat. Conf. Data Engrg. (ICDE'97) (1997) Birmingham, UK:232–243CrossrefGoogle Scholar
  • Balabanovic M., Shoham Y. Fab: Content-based, collaborative recommendation. Commun. ACM (1997) 40(3):66–72CrossrefGoogle Scholar
  • Bennet J., Lanning S. The Netflix prize. Proc. KDD Cup and Workshop 2007 at the 13th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (2007) San Jose, CAGoogle Scholar
  • Burke R., Brusilovsky P., Kobsa A., Nejdl W. Hybrid Web recommender systems. The Adaptive Web: Methods and Strategies of Web Personalization (2007) 4321(Springer-Verlag, Berlin) 377–408Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Ceri S., Gottlob G. Translating SQL into relational algebra: Optimization, semantics and equivalence of SQL queries. IEEE Trans. Software Engrg. (1985) 11(4):324–345CrossrefGoogle Scholar
  • Chaudhuri S., Dayal U. An overview of data warehousing and OLAP technology. ACM SIGMOD Record (1997) 26(1):65–74CrossrefGoogle Scholar
  • Cohen W. W., Schapire R. E., Singer Y. Learning to order things. J. Artificial Intelligence Res. (1999) 10:243–270CrossrefGoogle Scholar
  • Gyssens M., Lakshmanan L. V. S. A foundation for multi-dimensional databases. Proc. 23rd Internat. Conf. Very Large Data Bases (VLDB-97) (1997) Athens, Greece:106–115Google Scholar
  • Hill W., Stead L., Rosenstein M., Furnas G. Recommending and evaluating choices in a virtual community of use. Proc. Conf. Human Factors Comput. Systems (CHI'95) (1995) Boston:194–201CrossrefGoogle Scholar
  • Jameson A., Smyth B., Brusilovsky P., Kobsa A., Nejdl W. Recommendation to groups. The Adaptive Web: Methods and Strategies of Web Personalization (2007) 4321(Springer-Verlag, Berlin) 596–627Lecture Notes in Computer ScienceCrossrefGoogle Scholar
  • Koutrika G., Ikeda R., Bercovitz B., Garcia-Molina H. Flexible recommendations over rich data. Proc. 2008 ACM Conf. Recommender Systems (RecSys'08) (2008) Lausanne, Switzerland:203–210CrossrefGoogle Scholar
  • Li C., Wang X. S. A data model for supporting on-line analytical processing. Proc. 5th Internat. Conf. Inform. Knowledge Management (CIKM-1996) (1996) Rockville, MA:81–88CrossrefGoogle Scholar
  • Marcel P. Modeling and querying multidimensional databases: An overview. Networking Inform. Systems J. (1999) 2(5):515–548Google Scholar
  • Mild A., Reutterer T. Collaborative filtering methods for binary market basket data analysis. Lecture Notes in Computer Science (2001) 2252(Springer, Berlin/Heidelberg, Germany) 302–313Google Scholar
  • Miller B. N., Albert I., Lam S. K., Konstan J. A., Riedl J. MovieLens unplugged: Experiences with an occasionally connected recommender system. Proc. Internat. Conf. Intelligent User Interfaces (2003) Miami:263–266CrossrefGoogle Scholar
  • Resnick P., Iakovou N., Sushak M., Bergstrom P., Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. Proc. 1994 ACM Conf. Comput. Supported Cooperative Work (1994) Chapel Hill, NC:175–186CrossrefGoogle Scholar
  • Shardanand U., Maes P. Social information filtering: Algorithms for automating “word of mouth”. Proc. Conf. Human Factors Comput. Systems (1995) New York:210–217CrossrefGoogle Scholar
  • Snodgrass R. The temporal query language TQuel. ACM Trans. Database Systems (1987) 12(2):247–298CrossrefGoogle Scholar
  • Thomas H., Datta A. A conceptual model and algebra for online analytical processing in decision support databases. Inform. Systems Res. (2001) 12(1):83–102LinkGoogle Scholar
  • Umyarov A., Tuzhilin A. Improving collaborative filtering recommendations using external data. Proc. IEEE Internat. Conf. Data Mining (ICDM-2008) (2008) Pisa, Italy:618–627CrossrefGoogle Scholar
  • Wade W. A grocery cart that holds bread, butter and preferences. New York Times (2003) January 16):E6Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.