@inproceedings{p-v-ramanujan-2024-vector,
    title = "Vector Embedding Solution for Recommendation System",
    author = "P V, Vidya  and
      Ramanujan, Ajeesh",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.icon-1.68/",
    pages = "583--587",
    abstract = "We propose a vector embedding approach for recommendation systems aimed at identifying product affinities and suggesting complementary items. By capturing relationships between products, the model delivers highly relevant recommendations based on the context. A neural network is trained on purchase data to generate word embeddings, represented as a weight matrix. The resulting model predicts complementary products with top-20 and top-50 precision scores of 0.59251 and 0.29556, respectively. These embeddings effectively identify products likely to be co-purchased, enhancing the relevance and accuracy of the recommendations."
}