@inproceedings{rafieian-costa-jussa-2020-e,
title = "{E}-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors",
author = "Rafieian, Bardia and
Costa-juss{\`a}, Marta R.",
booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
month = dec,
year = "2020",
address = "Barcelona, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ecomnlp-1.1",
pages = "1--10",
abstract = "In this paper, we present two productive and functional recommender methods to improve the ac- curacy of predicting the right product for the user. One proposal is a survey-based recommender system that uses k-nearest neighbors. It recommends products by asking questions from the user, efficiently applying a binary product vector to the product attributes, and processing the request with a minimum error. The second proposal uses an enriched collaborative-based recommender system using enriched weighted vectors. Thanks to the style rules, the enriched collaborative- based method recommends outfits with competitive recommendation quality. We evaluated both of the proposals on a Kaggle fashion-dataset along with iMaterialist and, results show equivalent performance on binary gender and product attributes.",
}
Markdown (Informal)
[E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors](https://aclanthology.org/2020.ecomnlp-1.1) (Rafieian & Costa-jussà, EcomNLP 2020)
ACL