Recommendations for Car Selection System Using Item-Based Collaborative Filtering (CF)

Car is a four or more wheels transportation that have many benefits for humanity, one of which can carry passengers and stuffs. The technology that has been developed brings a lot of information, this is aligned with information related to the car. It often happens when someone who wants to choose a car becomes confused because so many cars information are available on the internet. Therefore, we need a system that can help provide information about cars that are in accordance with the user's wishes, namely the recommendation system. The recommendation system requires the right recommendation In this research will focus on the problem of recommending the car selection system by building a recommendation system through an item-based Collaborative Filtering approach. To help provide solutions to the above problems, this recommendation system has 9 parameters. The application of item-based Collaborative Filtering algorithm produces a recommendation system that has a Mean Absolute Error (MAE) of 0.202 and has an accuracy rate of 95.955%.

• Publication year

  2019

• Venue

  2019 IEEE International Conference on Signals and Systems (ICSigSys)

• Publication date

  2019-07-16

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/ICSIGSYS.2019.8811083
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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