Ensemble-based Top-k Recommender System Considering Incomplete Data | ||
| Journal of AI and Data Mining | ||
| مقاله 5، دوره 7، شماره 3، مهر 2019، صفحه 393-402 اصل مقاله (1.31 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2019.7026.1830 | ||
| نویسندگان | ||
| M. Moradi؛ J. Hamidzadeh* | ||
| Faculty of computer engineering and information technology, Sadjad University of Technology, Mashhad, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Top-k recommender systems؛ Incomplete data؛ Ensemble learning | ||
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آمار تعداد مشاهده مقاله: 907 تعداد دریافت فایل اصل مقاله: 1,256 |
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