Ensemble-based Top-k Recommender System Considering Incomplete Data
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 7، شماره: 3
سال انتشار: 1398
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 379
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شناسه ملی سند علمی:
JR_JADM-7-3_005
تاریخ نمایه سازی: 19 تیر 1398
چکیده مقاله:
Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. However, the latter is far from satisfactory because human decisions affected by environmental conditions and they might change over time. In this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. We also design an ensemble-based method to obtain Top-k recommendations. To evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross validation over real-world data sets. Experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding applied evaluation metrics.
کلیدواژه ها:
نویسندگان
M. Moradi
Faculty of computer engineering and information technology, Sadjad University of Technology, Mashhad, Iran.
J. Hamidzadeh
Faculty of computer engineering and information technology, Sadjad University of Technology, Mashhad, Iran.