Benchmarking Explicit Rating Prediction Algorithms for Cosmetic Products

Raditya Nurfadillah, Fariz Darari, Radityo Eko Prasojo, Yasmin Amalia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Recommendation systems have become a staple feature for any e-commerce sites. The ability to predict whether a customer likes an unseen product forms the very foundation of a recommendation system. In this paper, we concern the issue of explicit rating prediction over cosmetic products. Given a dataset of cosmetic product ratings, we analyze the characteristics of the dataset and implement a wide range of algorithms, such as KNN and matrix factorization, to predict such ratings. We evaluate the performance of these algorithms using MAE and RMSE measures, and discuss factors that may contribute to their performance results. Our experiments have shown that the SVD++ technique performs the best among all with an MAE of 0.7699 and an RMSE of 0.9696. We hope that our paper can shed new light on the selection of explicit rating prediction algorithms not only in the domain of beauty products, but also in wider scenarios.

Original languageEnglish
Title of host publication2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
EditorsFerry Wahyu Wibowo
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages457-462
Number of pages6
ISBN (Electronic)9781728184067
DOIs
Publication statusPublished - 10 Dec 2020
Event3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 - Yogyakarta, Indonesia
Duration: 10 Dec 2020 → …

Publication series

Name2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

Conference

Conference3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
Country/TerritoryIndonesia
CityYogyakarta
Period10/12/20 → …

Keywords

  • Benchmarking
  • Cosmetic Products
  • E-commerce
  • Explicit Rating
  • Recommender Systems

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