Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

Top-k queries are extensively used to retrieve the k most relevantoptions (e.g., products, services, accommodation alternatives, etc)based on a weighted scoring function that captures user preferences. In this paper, we take the viewpoint of a business owner whoplans to introduce a new option to the market, with a certain type ofclientele in mind. Given a target region in the consumer spectrum,we determine what attribute values the new option should have,so that it ranks among the top-k for any user in that region. Ourmethodology can also be used to improve an existing option, at theminimum modification cost, so that it ranks consistently high for anintended type of customers. This is the first work on competitiveoption placement where no distinct user(s) are targeted, but a general clientele type, i.e., a continuum of possible preferences. Herealso lies our main challenge (and contribution), i.e., dealing withthe interplay between two continuous spaces: the targeted regionin the preference spectrum, and the option domain (where the newoption will be placed). At the core of our methodology lies a noveland powerful interlinking between the two spaces. Our algorithmsoffer exact answers in practical response times, even for the largestof the standard benchmark datasets.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the VLDB Endowment: 45th VLDB 2019, Los Angeles, CA, August 26-30

First Page

1181

Last Page

1194

Identifier

10.14778/3339490.3339500

Publisher

VLDB Endowment

City or Country

Stanford, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.14778/3339490.3339500

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