TY - GEN
T1 - Skincare Recommender System Using Neural Collaborative Filtering with Implicit Rating
AU - Qalbyassalam, Chaira
AU - Rachmadi, Reza Fuad
AU - Kurniawan, Arief
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Skincare products are essential cosmetics for women, especially in this modern era. Many e-commerce services provide a variety of skincare products in their catalogs. One problem with purchasing skincare products online is that users cannot try the product and depend on other customers' rating reviews. However, rating reviews on a scale of 1 to 5 are considered insufficient to represent product quality, and users need to read review texts written by other users to get more specific information about the quality of the product. This paper investigated NCF (Neural Collaborative Filtering) for skincare recommender systems. Instead of using explicit rating as usually used on standard recommender systems, we adapted the sentiment score as a rating which, in our experiments, proved can improve the classifier's performance. We collected 180,104 rows of data with 11 data attributes and 1,339 skincare products to evaluate our proposed method. Experiments on the dataset show that the proposed NCF with explicit ratings achieved an RMSE of 0.8033, and the NCF with implicit ratings achieved an RMSE of 0.4931.
AB - Skincare products are essential cosmetics for women, especially in this modern era. Many e-commerce services provide a variety of skincare products in their catalogs. One problem with purchasing skincare products online is that users cannot try the product and depend on other customers' rating reviews. However, rating reviews on a scale of 1 to 5 are considered insufficient to represent product quality, and users need to read review texts written by other users to get more specific information about the quality of the product. This paper investigated NCF (Neural Collaborative Filtering) for skincare recommender systems. Instead of using explicit rating as usually used on standard recommender systems, we adapted the sentiment score as a rating which, in our experiments, proved can improve the classifier's performance. We collected 180,104 rows of data with 11 data attributes and 1,339 skincare products to evaluate our proposed method. Experiments on the dataset show that the proposed NCF with explicit ratings achieved an RMSE of 0.8033, and the NCF with implicit ratings achieved an RMSE of 0.4931.
KW - Neural Collaborative Filtering
KW - Skincare Recommender System
KW - sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85149128299&partnerID=8YFLogxK
U2 - 10.1109/CENIM56801.2022.10037471
DO - 10.1109/CENIM56801.2022.10037471
M3 - Conference contribution
AN - SCOPUS:85149128299
T3 - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
SP - 272
EP - 277
BT - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
Y2 - 22 November 2022 through 23 November 2022
ER -