Furniture Recommendations Based on User Propensity and Furniture Style Compatibility
Author:
Affiliation:
1. Master’s Program in Information and Computer Science, Doshisha University, Kyoto, Japan
2. Department of Intelligent Information Engineering and Sciences, Doshisha University, Kyoto, Japan
Funder
JSPS KAKENHI
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10430090.pdf?arnumber=10430090
Reference29 articles.
1. Towards practical visual search engine within elasticsearch;Mu;arXiv:1806.08896,2018
2. Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions
3. Recommendation system development for fashion retail e-commerce
4. Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System
5. A multi-level collaborative filtering method that improves recommendations
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