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
- Venue:
- EcomNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/2020.ecomnlp-1.1
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.ecomnlp-1.1.pdf