Combinatorial search
Combining fuzzy information from multiple systems (extended abstract)
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
WALRUS: a similarity retrieval algorithm for image databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Bundling Information Goods: Pricing, Profits, and Efficiency
Management Science
Dynamic Programming and Its Application
Dynamic Programming and Its Application
Similarity Searching in Medical Image Databases
IEEE Transactions on Knowledge and Data Engineering
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Collaborative pricing model for bundling information goods
Journal of Information Science
Combinatorial auctions with k-wise dependent valuations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Electronic Commerce Research and Applications
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Economics research has long recognized that bundling enables savings in production and transaction costs, promotes complementary among the bundle components and sorts consumers according to their valuations. Sellers employ market analysis and intelligence to extract the most surplus. In the age of electronic commerce with low product information access cost, buyers can take advantage of the benefits of bundling by performing dynamic composition of goods from multiple companies offering heterogeneous products and services. These goods, with the proper mix of sources and quantity, may offer additional discounts and benefits, which would not have risen should purchase decisions were made independently. A prominent example is packaged travel, which often involves air, hotel and car rentals. An optimal travel package search not only takes advantage of the lowest available prices of air, hotel and car rental individually but also exploits various discounts through business partnerships between service providers.Today's database infrastructure to support the search of dynamically bundled goods, however, is insufficient. The complex search operations involving cross join of many product categories with hundreds or thousands of offerings can be formulated as SQL queries. But executing these queries in a traditional database is inefficient. This paper proposes an I/O conscious, dynamic programming based algorithm for bundle search. The proposed algorithm finds the top-K combinations of goods abstracted by a linear relationship graph. Experimental results indicate that the proposed algorithm achieves more than two orders of magnitude speedup over cross join, and it is more than an order of-magnitude faster than the simple dynamic programming solution. The performance gap further widens as the number of product categories and the number of offerings within each category increase. This paper characterizes the computational and I/O complexity of the proposed algorithm and suggests extensions to search bundles with more complex relationships.