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
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Constraint-based recommender systems: technologies and research issues
Proceedings of the 10th international conference on Electronic commerce
Content-based recommendation systems
The adaptive web
Recommendation systems with complex constraints: A course recommendation perspective
ACM Transactions on Information Systems (TOIS)
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
Beyond rating prediction accuracy: on new perspectives in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
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We consider the problem of recommending the best set of k items when there is an inherent ordering between items, expressed as a set of prerequisites (e.g., the course `Real Analysis' is a prerequisite of `Complex Analysis'). Since this problem is NP-hard, we develop 3 approximate algorithms to solve this problem. We experimentally evaluate these algorithms on synthetic data.