@inproceedings{angelovska-etal-2021-siamese,
    title = "{S}iamese Neural Networks for Detecting Complementary Products",
    author = "Angelovska, Marina  and
      Sheikholeslami, Sina  and
      Dunn, Bas  and
      Payberah, Amir H.",
    editor = "Sorodoc, Ionut-Teodor  and
      Sushil, Madhumita  and
      Takmaz, Ece  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.eacl-srw.10/",
    doi = "10.18653/v1/2021.eacl-srw.10",
    pages = "65--70",
    abstract = "Recommender systems play an important role in e-commerce websites as they improve the customer journey by helping the users find what they want at the right moment. In this paper, we focus on identifying a complementary relationship between the products of an e-commerce company. We propose a content-based recommender system for detecting complementary products, using Siamese Neural Networks (SNN). To this end, we implement and compare two different models: Siamese Convolutional Neural Network (CNN) and Siamese Long Short-Term Memory (LSTM). Moreover, we propose an extension of the SNN approach to handling millions of products in a matter of seconds, and we reduce the training time complexity by half. In the experiments, we show that Siamese LSTM can predict complementary products with an accuracy of {\textasciitilde}85{\%} using only the product titles."
}