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            <title>Proceedings of Workshop on Natural Language Processing in E-Commerce</title>
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            <namePart type="given">Huasha</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors
%A Rafieian, Bardia
%A Costa-jussà, Marta R.
%Y Zhao, Huasha
%Y Sondhi, Parikshit
%Y Bach, Nguyen
%Y Hewavitharana, Sanjika
%Y He, Yifan
%Y Si, Luo
%Y Ji, Heng
%S Proceedings of Workshop on Natural Language Processing in E-Commerce
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain
%F rafieian-costa-jussa-2020-e
%X 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.
%U https://aclanthology.org/2020.ecomnlp-1.1
%P 1-10

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)

• E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors (Rafieian & Costa-jussà, EcomNLP 2020)

ACL

• Bardia Rafieian and Marta R. Costa-jussà. 2020. E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors. In Proceedings of Workshop on Natural Language Processing in E-Commerce, pages 1–10, Barcelona, Spain. Association for Computational Linguistics.