کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
528201 869535 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Efficient recommendation methods using category experts for a large dataset
ترجمه فارسی عنوان
روش های توصیه کارآمد با استفاده از متخصصان رده برای مجموعه داده های بزرگ
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• We propose four recommendation methods by exploiting the category experts.
• Category experts are maintained in up-to-date through the incremental update.
• Our methods satisfy both the performance as well as accuracy.
• In terms of coverage, our methods overcome the existing methods.

Neighborhood-based methods have been proposed to satisfy both the performance and accuracy in recommendation systems. It is difficult, however, to satisfy them together because there is a tradeoff between them especially in a big data environment. In this paper, we present a novel method, called a CE method, using the notion of category experts in order to leverage the tradeoff between performance and accuracy. The CE method selects a few users as experts in each category and uses their ratings rather than ordinary neighbors’. In addition, we suggest CES and CEP methods, variants of the CE method, to achieve higher accuracy. The CES method considers the similarity between the active user and category expert in ratings prediction, and the CEP method utilizes the active user’s preference (interest) on each category. Finally, we combine all the approaches to create a CESP method, considering similarity and preference simultaneously. Using real-world datasets from MovieLens and Ciao, we show that our proposal successfully leverages the tradeoff between the performance and accuracy and outperforms existing neighborhood-based recommendation methods in coverage. More specifically, the CESP method provides 5% improved accuracy compared to the item-based method while performing 9 times faster than the user-based method.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Fusion - Volume 28, March 2016, Pages 75–82
نویسندگان
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