RANCANGAN SISTEM REKOMENDASI GAME DENGAN MODEL-BASED COLLABORATION FILTERING
DOI:
https://doi.org/10.61769/telematika.v6i1.40Keywords:
Sistem rekomendasi, game, model-based collaborative filteringAbstract
Pengguna internet saat ini makin kewalahan menghadapi arus informasi yang makin deras di internet. Begitu banyak produk dan informasi yang disajikan, namun tidak ada
panduan mana produk atau informasi yang memang benar-benar dibutuhkan setiap user dan mana yang sebenarnya tidak perlu. Di tengah kebingungan ini, kebanyakan user akhirnya memilih untuk meminta rekomendasi dari lingkungan sekitarnya sebelum memilih atau membeli sebuah produk tertentu. Beberapa situs saat ini mulai menyediakan fitur rekomendasi yang mampu memberikan saran produk mana yang dianggap paling cocok untuk masingmasing user. Salah satu metode yang dapat dipakai untuk merancang sistem rekomendasi ini adalah metode model-based collaborative filtering. Makalah ini berisi rancangan sebuah sistem rekomendasi berdasarkan metode model-based collaborative filtering untuk kasus rekomendasi game. Kebanyakan situs game yang ada hanya menyediakan rekomendasi berdasarkan kesamaan genre dari game yang dimiliki. Dalam rancangan yang disajikan di makalah ini, rekomendasi yang diberikan didasarkan kepada kemiripan antar game dan sejarah transaksi yang pernah
dilakukan oleh user.
Nowadays, internet users are becoming more and more overwhelmed with the phenomenon of information overload. There are so many products and informations available on the internet, but the users have absolutely no guidance on telling which information is important for them and which is not. In this kind of situation, users usually seek for recommendations from their
surroundings before they actually choose or purchase a product. Some websites today have already included a recommender system that able to suggest which products are most suitable for certain user. One of the method that commonly used in building a recommender system is model-base collaborative filtering. This paper introduces a design for a game recommender system based on model-based collaborative filtering. Most game websites availbale today only give recommendations based on the category of the games. In the design presented in this paper, the
recommendations are based on the similarity between games and the history of user transactions.
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