PENGEMBANGAN METODE MULTICRITERIA COLLABORATIVE FILTERING PADA SISTEM REKOMENDASI DOKUMEN - , W I R A N T O ([Yogyakarta] : Universitas Gadjah Mada, 2014)
The main problem faced by the collaborative filtering-based recommender systems is the prediction accuracy and precision of recommendation. This research focuses on developing an algorithm of the prediction of ratings and the diversity exploration on a multicriteria collaborative filtering-based document recommender system. The development aims at improving the prediction accuracy and the precision of recommendations. The approaches used to improve the prediction accuracy are multirating matrix decomposition, multidimensional distance-based user similarity, the calculation of individual criteria weights, and the rating prediction for the overall criteria by using a combination approach, while the approach used to improve the precision of recommendation was to apply the concept of content-based and criteria-based diversity. Result of the research was the model of the document recommender system based on multicriteria collaborative filtering with characteristics as follows : (1) the rating prediction for the four individual criteria of documents used the pure algorithm of collaborative filtering, (2) rating prediction for the overall criteria used a combination algorithm, (3) the measurement of user similarity with a cosine approach and the concept of multidimensional distance, (4) the accommodation of diversity in the generation of recommendations. The testing of model was done on three conditions determined based on the number of users and documents, i.e., 50x100, 100x200 and 200x400 with variation in matrix sparsity rate of 10%, 20 %, 30 %, 40 %, 50 % and 60 %. Metric used to measure the prediction accuracy was the Mean Absolute Error (MAE), while the rate of the precision of recommendation was measured by using a percentage of Top-N. Based on the results of testing, it can be concluded that the multicriteria collaborative filtering method developed had much better performance than multicriteria collaborative filtering deceloped by previous researchers, which was characterized by the increasingly smaller values of MAE and a greater percentage of recommendation Top-N. The best performance was achieved by the model of Multicriteria Collaborative Filtering system that used multidimensional distance-based similarity. The application of the concept of document diversity on the generation of recommendation could contribute significantly to the increase of the precision of recommendation.