Propensity-scored Probabilistic Label Trees

Marek Wydmuch, Kalina Jasinska-Kobus, Rohit Babbar, Krzysztof Dembczynski

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels. Recently, XMLC has been widely applied to diverse web applications such as automatic content labeling, online advertising, or recommendation systems. In such environments, label distribution is often highly imbalanced, consisting mostly of very rare tail labels, and relevant labels can be missing. As a remedy to these problems, the propensity model has been introduced and applied within several XMLC algorithms. In this work, we focus on the problem of optimal predictions under this model for probabilistic label trees, a popular approach for XMLC problems. We introduce an inference procedure, based on the A*-search algorithm, that efficiently finds the optimal solution, assuming that all probabilities and propensities are known. We demonstrate the attractiveness of this approach in a wide empirical study on popular XMLC benchmark datasets.
AlkuperäiskieliEnglanti
OtsikkoSIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
KustantajaACM
Sivut2252–2256
Sivumäärä5
ISBN (elektroninen)978-1-4503-8037-9
DOI - pysyväislinkit
TilaJulkaistu - 11 heinäkuuta 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational ACM SIGIR Conference on Research and Development in Information Retrieval - Virtual, Online, Kanada
Kesto: 11 heinäkuuta 202115 heinäkuuta 2021
Konferenssinumero: 44

Conference

ConferenceInternational ACM SIGIR Conference on Research and Development in Information Retrieval
LyhennettäSIGIR
Maa/AlueKanada
KaupunkiVirtual, Online
Ajanjakso11/07/202115/07/2021

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