ECTI TRANSACTIONS ON COMPUTER INFORMATION TECHNOLOGYVolume 12, No. 02, Month NOVEMBER, Year 2018, Pages 106 - 117
Top-k recommended items: applying clustering technique for recommendation
Kittisak Onuean, Sunantha Sodsee, Phayung Meesad
Abstract Download PDFThis research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique.
Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some
items set for user’s preference. In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets
(1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all 98,903 items being build and test the models. Methods was divided into three parts included 1) Simple Agent Module 2) Neighbor Filtering and 3) Prediction
Recommender System, Clustering Technique, Data Sparsity