Workload-driven learning of mallows mixtures with pairwise preference data

Julia Stoyanovich, Lovro Ilijasic, Haoyue Ping

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

    Abstract

    In this paper we present a framework for learning mixtures of Mallows models from large samples of incomplete preferences. The problem we address is of significant practical importance in social choice, recommender systems, and other domains where it is required to aggregate, or otherwise analyze, preferences of a heterogeneous user base. We improve on state-of-the-art methods for learning mixtures of Mallows models with pairwise preference data. Exact sampling from the Mallows posterior in presence of arbitrary pairwise evidence is known to be intractable even for a single Mallows. This motivated to the development of an approximate sampler called AMP. In this paper we propose AMPx, an ensemble method for approximate sampling from the Mallows posterior that combines AMP with frequency-based estimation of posterior probabilities. We experimentally demonstrate that AMPx achieves faster convergence and higher accuracy than AMP alone. We also adapt stateof-the-art clustering techniques that have not been used in this setting, for learning parameters of the Mallows mixture, and show experimentally that mixture parameters can be learned accurately and efficiently.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 19th International Workshop on Web and Databases, WebDB 2016
    PublisherAssociation for Computing Machinery, Inc
    ISBN (Electronic)9781450343107
    DOIs
    StatePublished - Jun 26 2016
    Event19th International Workshop on Web and Databases, WebDB 2016 - San Francisco, United States
    Duration: Jun 26 2016Jul 1 2016

    Publication series

    NameProceedings of the 19th International Workshop on Web and Databases, WebDB 2016

    Other

    Other19th International Workshop on Web and Databases, WebDB 2016
    CountryUnited States
    CitySan Francisco
    Period6/26/167/1/16

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Information Systems

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  • Cite this

    Stoyanovich, J., Ilijasic, L., & Ping, H. (2016). Workload-driven learning of mallows mixtures with pairwise preference data. In Proceedings of the 19th International Workshop on Web and Databases, WebDB 2016 (Proceedings of the 19th International Workshop on Web and Databases, WebDB 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/2932194.2932202