@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://aclanthology.org/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."
}
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%0 Conference Proceedings
%T Vector Embedding Solution for Recommendation System
%A P V, Vidya
%A Ramanujan, Ajeesh
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F p-v-ramanujan-2024-vector
%X 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.
%U https://aclanthology.org/2024.icon-1.68/
%P 583-587
Markdown (Informal)
[Vector Embedding Solution for Recommendation System](https://aclanthology.org/2024.icon-1.68/) (P V & Ramanujan, ICON 2024)
ACL
- Vidya P V and Ajeesh Ramanujan. 2024. Vector Embedding Solution for Recommendation System. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 583–587, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).