Abstract
Recently, reverse rank queries have attracted significant research interest. They have real-life applicability, such as in marketing analysis and product placement. Reverse k-ranks queries return users (preferences) who favor a given product more than other people. This helps manufacturers find potential buyers even for an unpopular product. Similar to the cable television industry, which often bundles channels, manufacturers are also willing to offer several products for sale as one combined product for marketing purposes.
Unfortunately, current reverse rank queries, including Reverse k-ranks queries, only consider one product. To address this limitation, we propose the aggregate reverse rank queries to find matching user preferences for a set of products. To resolve this query more efficiently, we propose the concept of pre-processing the preference set and determining its upper and lower bounds. Combining these bounds with the query set, we proposed and implemented the tree pruning method (TPM) and double-tree method (DTM). The theoretical analysis and experimental results demonstrated the efficacy of the proposed methods.
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- 1.
The geometric view is that there exists a hyper-plane \(H(w_t^{(j)})\) that first touches q rather than others.
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NBA: http://www.databasebasketball.com/; HOUSE: https://usa.ipums.org/usa/.
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© 2016 Springer International Publishing Switzerland
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Dong, Y., Chen, H., Furuse, K., Kitagawa, H. (2016). Aggregate Reverse Rank Queries. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_8
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DOI: https://doi.org/10.1007/978-3-319-44406-2_8
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