Abstract:
The development of information technology has given a lot of influence and changes in human
life. One of the areas of life affected is the field of trade. Currently, many people are looking for
products to buy manually by going around looking for stores, and the results are wasted time,
energy, and transportation costs. In addition, by buying manually, the buyer does not know the
experience of other buyers who bought the same model. Also, users or customers become
overwhelmed with the vast amount of information available to them, and it is challenging for them
to make a final choice from multiple products about the products to choose.
Based on this background, this research work has developed a product recommender system that
considers multiple variables like product brand, product value, product catalogue and so on for
assisting customers in making a decision. The developed system filters information from the
Amazon product database to recommend products to customers. Content-based filtering and
collaborative filtering model to recommend products to prospective customers. The results from
the evaluation of the research work show that the precision, accuracy, F1 score and recall have
better and high performance and very low computational time.
