Recommender System Using Collaborative Filtering A Case Study of Toyota Buzz Company Limited
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Abstract
The objective of this research was to develop a recommender system by using collaborative filtering to analyze the users’ behavior. This system will provide suitable car model recommendations for the customer’s needs. The developed system will help customers to be satisfied with their products in a short time. The method was conducted by determining the relationship between the customer data and the car model list obtained from the previous car sales history dataset. This study focused on analyzing 44,079 sales transactions for 24 car models and 43,098 customers. These transactions were under the condition of complete car delivery to customers. The data were stored in the MySQL database. The user-based similarity algorithm along with the cosine similarity equation, which is a function in Python, were used to analyze customers with similar behavior. The developed system could be applied to recommend car models to meet customer needs in an accurate and efficient manner with a mean absolute error (MAE) of 0.97. In conclusion, the performance of the developed system was at a good level and it could be practically applied.
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