ANALISIS SISTEM REKOMENDASI COLLABORATIVE FILTERING PADA DATA TERNORMALISASI; COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS ANALYSIS ON NORMALIZED DATA

Wicaksono, Pandu, Lukman Heryawan

2015 | Skripsi | FMIPA

The rapid growth in volume and variety of information found on the web has its own downside: finding relevant information become somewhat more difficult. This problem has led to the development of recommender systems which filters information to the likings of a certain user. One of the most popular algorithm used for recommendation systems is Collaborative Filtering (CF). CF is widely used because it delivers a more personalized recommendation thus producing different recommendations for different users based on their preference. There are various efforts that can be done to increase CF prediction accuracy, one of them is through data normalization. This research will apply two CF algortihms, user-based and item-based CF with three different similarity measurement method for each algorithm. The similarity measurement methods used are cosine similarity, pearson correlation, and adjusted cosine similarity. These various algorithms performance will then be tested and compared on data both with and without normalization. The normalization method used in this research is modified standard score which will be applied on the MovieLens dataset from the movie recommendation website movielens.org developed by the GroupLens research team. Result shows that data normalization with modified standard score improved the accuracy of all variations of the item-based CF algorithm with the highest improvement of 3,63%, but only managed to improve user-based CF just on the adjusted cosine method. The highest accuracy both before and after data normalization was achieved by user-based CF with the adjusted cosine similarity method. This proves that user-based CF can produce better accuracy than item-based CF.

Kata Kunci : Recommendation system; Collaborative Filtering; Similarity measurement; Normalization; Accuracy