E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors

Bardia Rafieian, Marta R. Costa-jussà


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.
Anthology ID:
2020.ecomnlp-1.1
Volume:
Proceedings of Workshop on Natural Language Processing in E-Commerce
Month:
Dec
Year:
2020
Address:
Barcelona, Spain
Editors:
Huasha Zhao, Parikshit Sondhi, Nguyen Bach, Sanjika Hewavitharana, Yifan He, Luo Si, Heng Ji
Venue:
EcomNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/2020.ecomnlp-1.1
DOI:
Bibkey:
Cite (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.
Cite (Informal):
E-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors (Rafieian & Costa-jussà, EcomNLP 2020)
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https://aclanthology.org/2020.ecomnlp-1.1.pdf