Aditya Parameswaran
Hector Garcia-Molina
Abstract
We study the problem of making recommendations when the objects to be recommended must also satisfy constraints or requirements. In particular, we focus on course recommendations: the courses taken by a student must satisfy requirements (e.g., take two out of a set of five math courses) in order for the student to graduate. Our work is done in the context of the CourseRank system, used by students to plan their academic program at Stanford University. Our goal is to recommend to these students courses that not only help satisfy constraints, but that are also desirable (e.g., popular or taken by similar students). We develop increasingly expressive models for course requirements, and present a variety of schemes for both checking if the requirements are satisfied, and for making recommendations that take into account the requirements. We show that some types of requirements are inherently expensive to check, and we present exact, as well as heuristic techniques, for those cases. Although our work is specific to course requirements, it provides insights into the design of recommendation systems in the presence of complex constraints found in other applications.
- Adomavicius, G. and Tuzhilin, E. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Engin. 17, 734--749. Google Scholar
Digital Library
- Barahona, F. and Jensen, D. 1998. Plant location with minimum inventory. Math. Program. 83, 1, 101--111. Google ScholarDigital Library
- Bokhari, S. H. 1988. Partitioning problems in parallel, pipeline, and distributed computing. IEEE Trans. Comput. 37, 1, 48--57. Google ScholarDigital Library
- Burke, R. 2000. Knowledge-based recommender systems. In Encyclopedia of Library and Information Systems, Vol. 69.Google Scholar
- Burke, R. 2002. Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact. 12. Google ScholarDigital Library
- Ceria, S. 1996. CPLEX 3.0.Google Scholar
- Edmonds, J. and Karp, R. M. 1972. Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19, 248--264. Google ScholarDigital Library
- Felfernig, A. and Burke, R. D. 2008. Constraint-based recommender systems: Technologies and research issues. In Proceedings of the 10th International Conference on Electronic Commerce. 1--10. Google ScholarDigital Library
- Ford, L. R. and Fulkerson, D. R. 1962. Flows in Networks. Princeton University Press, Princeton, NJ.Google Scholar
- Geoffrion, A. M. 1967. Integer programming by implicit enumeration and balas' method. SIAM Review, 178--190.Google Scholar
- Gomory, R. E. 1958. Outline of an algorithm for integer solutions to linear programs. Bull. Amer. Math. Soc. 64, 5, 275--278.Google ScholarCross Ref
- Karimzadehgan, M. and Zhai, C. 2009. Constrained multi-aspect expertise matching for committee review assignment. In Proceedings of the ACM International Conference on Information and Knowledge Management, ACM, New York, NY, 1697--1700. Google ScholarDigital Library
- Lappas, T., Liu, K., and Terzi, E. 2009. Finding a team of experts in social networks. In Proceedings of the International SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, New York, NY. 467--476. Google ScholarDigital Library
- Lawler, E. L. 1972. A procedure for computing the k best solutions to discrete optimization problems and its application to the shortest path problem. Manage. Sci. 18, 7, 401--405.Google Scholar
- Levner, E. V. and Nemirovsky, A. S. 1994. A network flow algorithm for just-in-time project scheduling. Euro. J. Rev. Res. 79, 2, 167--175.Google Scholar
- Mahendrarajah, A. and Fiala, F. 1976. A comparison of three algorithms for linear zero-one programs. ACM Trans. Math. Softw. 2, 4, 331--334. Google ScholarDigital Library
- Makhorin, A. 2000. Gnu linear programming toolkit.Google Scholar
- McNee, S. M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S. K., Rashid, A. M., Konstan, J. A., and Riedl, J. 2002. On the recommending of citations for research papers. In Proceedings of the Conference on Supported Cooperative Work, ACM, New York, NY, 116--125. Google ScholarDigital Library
- Nemhauser, G. and Savelsbergh, M. 2004. Minto 3.1.Google Scholar
- Parameswaran, A., Garcia-Molina, H., and Ullman, J. D. 2010. Evaluating combining and generalizing recommendations with prerequisites. In Proceedings of the ACM International Conference on Information and Knowledge Management, ACM, New York, NY, 919--928. Google ScholarDigital Library
- Parameswaran, A. G., Koutrika, G., Bercovitz, B., and Garcia-Molina, H. 2010. Recsplorer: recommendation algorithms based on precedence mining. In Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM, New York, NY, 87--98. Google ScholarDigital Library
- Pazzani, M. J. and Billsus, D. 2007. Content-based recommendation systems. In The Adaptive Web, 325--341. Google ScholarDigital Library
- Russell, S. and Norvig, P. 2003. Artificial Intelligence: A Modern Approach 2nd Ed. Prentice-Hall, NJ. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the International World Wide Web Conference, ACM, New York, NY, 285--295. Google ScholarDigital Library
- Tarjan, R. E. 1983. Data Structures and Network Algorithms. SIAM. Google ScholarDigital Library
- Wu, Y., Chou, P., Zhang, Q., Jain, K., Zhu, W., and Kung, S.-Y. 2005. Network planning in wireless ad hoc networks: A cross-layer approach. IEEE Select. Areas Comm. J. 23, 1, 136--150. Google ScholarDigital Library
- Xiao, L., Johansson, M., and Boyd, S. 2004. Simultaneous routing and resource allocation via dual decomposition. IEEE Trans. Comm. 52, 7, 1136--1144.Google ScholarCross Ref
- Xie, M., Lakshmanan, L. V., and Wood, P. T. 2010. Breaking out of the box of recommendations: from items to packages. In Proceedings of RecSys. 151--158. Google ScholarDigital Library
- Zanker, M. 2008. A collaborative constraint-based meta-level recommender. In Proceedings of RecSys. 139--146. Google ScholarDigital Library
- Zanker, M. and Jessenitschnig, M. 2009. Case-studies on exploiting explicit customer requirements in recommender systems. User Model. User-Adapt. Interact. 19, 133--166. Google ScholarDigital Library
Index Terms
- Recommendation systems with complex constraints: A course recommendation perspective
Recommendations
New Recommendation Techniques for Multicriteria Rating Systems
Traditional single-rating recommender systems have been successful in a number of personalization applications, but the research area of multicriteria recommender systems has been largely untouched. Taking full advantage of multicriteria ratings in ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Item recommendation in collaborative tagging systems via heuristic data fusion
Collaborative tagging systems have been popular on the Web. However, information overload results in the increasing need for recommender services from users, and thus item recommendation has been one of the key issues in such systems. In this paper, we ...
Comments