Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
Journal of the ACM (JACM)
Approximation algorithms
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
IEEE Transactions on Knowledge and Data Engineering
Finding and approximating top-k answers in keyword proximity search
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
Ranking objects based on relationships and fixed associations
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Finding a team of experts in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust and efficient algorithms for rank join evaluation
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Recommendations with prerequisites
Proceedings of the third ACM conference on Recommender systems
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Constructing and exploring composite items
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Automatic construction of travel itineraries using social breadcrumbs
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Recommendation systems with complex constraints: A course recommendation perspective
ACM Transactions on Information Systems (TOIS)
Evaluation of set-based queries with aggregation constraints
Proceedings of the 20th ACM international conference on Information and knowledge management
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
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
Beyond rating prediction accuracy: on new perspectives in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
IPS: an interactive package configuration system for trip planning
Proceedings of the VLDB Endowment
Composite retrieval of heterogeneous web search
Proceedings of the 23rd international conference on World wide web
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Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender system, focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of generating the top recommendation (package) is NP-complete, we devise several approximation algorithms for generating top-k packages as recommendations. We analyze their efficiency as well as approximation quality. Finally, using two real and two synthetic data sets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for top-k packages compared to exact algorithms.