| Autor: |
Shwu-Min Horng, C. L. Chao, Y. Y. Chang |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Advances in Data Science and Information Engineering ISBN: 9783030717032 |
| DOI: |
10.1007/978-3-030-71704-9_13 |
| Popis: |
Recommendation systems have been developed for online services with a simple product mix. This study extended its application to an offline retailer with a more complex product mix. Purchasing records of two thousand members within one year from an offline retailer in Taiwan were used as the dataset for the study. Datasets of the first 9 months were used for training and models were tested by the records of the last 3 months. This study developed a recommendation system by integrating a matrix factorization to uncover latent factors from both customers and items, and an evolutionary program to optimize the parameter settings of duration adjustment functions that were applied to assign weights so that past purchasing records closer to the testing period would receive higher weights. The objective of the system is to predict the likelihood of customers’ purchasing behavior toward items they never purchased during the training period. By measuring the average percentage-ranking of items for two thousand members, the recommendation system developed in this study outperformed two other approaches, popularity and item-based nearest neighborhood systems. In addition, academic and practical contributions were also discussed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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