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Title: Aggregate Reverse Rank Queries

Citation Type: Conference Paper

Publication Year: 2016

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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.

Url: https://link.springer.com/chapter/10.1007/978-3-319-44406-2_8

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Authors: Dong, Yuyang; Chen, Hanxiong; Furuse, Kazutaka; Kitagawa, Hiroyuki

Conference Name: Database and Expert Systems Applications

Publisher Location: Porto, Portugal

Data Collections: IPUMS USA

Topics: Other

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