Data structures and network algorithms
Data structures and network algorithms
Partitioning Problems in Parallel, Pipeline, and Distributed Computing
IEEE Transactions on Computers
Plant location with minimum inventory
Mathematical Programming: Series A and B
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
A Comparison of Three Algorithms for Linear Zero-One Programs
ACM Transactions on Mathematical Software (TOMS)
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
IEEE Transactions on Knowledge and Data Engineering
Constraint-based recommender systems: technologies and research issues
Proceedings of the 10th international conference on Electronic commerce
A collaborative constraint-based meta-level recommender
Proceedings of the 2008 ACM conference on Recommender systems
Case-studies on exploiting explicit customer requirements in recommender systems
User Modeling and User-Adapted Interaction
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained multi-aspect expertise matching for committee review assignment
Proceedings of the 18th ACM conference on Information and knowledge management
Content-based recommendation systems
The adaptive web
Recsplorer: recommendation algorithms based on precedence mining
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Breaking out of the box of recommendations: from items to packages
Proceedings of the fourth ACM conference on Recommender systems
Evaluating, combining and generalizing recommendations with prerequisites
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Network planning in wireless ad hoc networks: a cross-Layer approach
IEEE Journal on Selected Areas in Communications
Recommendations with prerequisites
Proceedings of the third ACM conference on Recommender systems
Evaluating, combining and generalizing recommendations with prerequisites
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Information seeking: convergence of search, recommendations, and advertising
Communications of the ACM
On the complexity of package recommendation problems
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Incremental set recommendation based on class differences
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
On the complexity of query result diversification
Proceedings of the VLDB Endowment
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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.