A Comparative Analysis of Memory-based and Model-based Collaborative Filtering on the Implementation of Recommender System for Ecommerce in Indonesia
Published in 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2016
Abstract
The increasing growth of e-commerce industry in Indonesia motivates e-commerce sites to provide better services to its customer. One of the strategies to improves e-commerce services is by providing personal recommendation, which can be done using recommender systems. However, there is still lack of studies exploring the best technique to implement recommender systems for e-commerce in Indonesia. This study compares the performance of two implementation approaches of collaborative filtering, which are memory-based and model-based, using data sample of PT X e-commerce. The performance of each approach was evaluated using offline testing and user-based testing. The result of this study indicates that the model-based recommender system is better than memory-based recommender system in three aspects: a) the accuracy of recommendation, b) computation time, and c) the relevance of recommendation. For number of transaction less than 300,000 in database, respondents perceived that the computation time of memory-based recommender system is tolerable, even though the computational time is longer than model-based.
Recommended citation: Aditya, P. H., Budi, I., & Munajat, Q. (2016). A Comparative Analysis of Memory-based and Model-based Collaborative Filtering on the Implementation of Recommender System for Ecommerce in Indonesia. 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 303-308.