Rank-aware clustering of structured datasets

Julia Stoyanovich, Sihem Amer-Yahia

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In online applications such as Yahoo! Personals and Yahoo! Real Estate users define structured profiles in order to find potentially interesting matches. Typically, profiles are evaluated against large datasets and produce thousands of matches. In addition to filtering, users also specify ranking in their profile, and matches are returned in a ranked list. Top results in a list are typically homogeneous, which hinders data exploration. For example, a user looking for 1- or 2-bedroom apartments sorted by price will see a large number of cheap 1-bedrooms in undesirable neighborhoods before seeing a different apartment. An alternative to ranking is to group matches on common attribute values, e.g., cheap 1-bedrooms in good neighborhoods, 2-bedrooms with 2 baths, and choose groups in relationship with ranking. In this paper, we present a novel paradigm of rank-aware clustering, and demonstrate its effectiveness on a large dataset from Yahoo! Personals, a leading online dating site.

    Original languageEnglish (US)
    Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
    Pages1429-1432
    Number of pages4
    DOIs
    StatePublished - 2009
    EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
    Duration: Nov 2 2009Nov 6 2009

    Publication series

    NameInternational Conference on Information and Knowledge Management, Proceedings

    Other

    OtherACM 18th International Conference on Information and Knowledge Management, CIKM 2009
    Country/TerritoryChina
    CityHong Kong
    Period11/2/0911/6/09

    Keywords

    • Information filtering
    • Information presentation
    • Rank-aware clustering
    • Structured datasets

    ASJC Scopus subject areas

    • General Business, Management and Accounting
    • General Decision Sciences

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