A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: A case study PT X

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.