Locally Constructing Product Taxonomies from Scratch Using Representation Learning

November 12, 2020

Abstract

Given a domain-specific set of concepts, local taxonomy construction (LTC) is the problem of ‘locally’ inducing the neighborhood of a concept (from the set of target concepts) without being given any example links. The problem, despite having practical importance, has received little research attention due to its difficulty (in contrast with link prediction, a problem that resembles it and has undergone broad study). In this paper, we present a formalism and deep empirical study on the LTC problem. In particular, we show that an innovative application of representation learning approaches from the natural language community could be adapted to tackle the problem, often quite effectively. We also present a detailed information retrieval (IR)-based methodology for evaluating these solutions on three realworld product datasets of varying sizes. To the best of our knowledge, this is the first paper to introduce the LTC problem, especially for e-commerce applications, and offer effective, nearly unsupervised, solutions, for addressing it on real-world data.

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Publication Type

Paper

Conference / Journal Name

ASONAM 2020

Authors

BibTeX


@inproceedings{
    author = {},
    title = {‌Locally Constructing Product Taxonomies from Scratch Using Representation Learning‌},
    booktitle = {Proceedings of ASONAM 2020‌},
    year = {‌2020‌}
}