کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
403560 677270 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems
ترجمه فارسی عنوان
توانمند سازی چندین دیدگاه اعتماد و شباهت به منظور افزایش سیستم های پیشنهاد دهنده خوشه ای
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item- and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 74, January 2015, Pages 14–27
نویسندگان
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