βP: A novel approach to filter out malicious rating profiles from recommender systems

کارشناسی ارشد و دکتری - کافه تدریس - وبینارهای حل سوالات کنکور ارشد

βP: A novel approach to filter out malicious rating profiles from recommender systems
Abstract: Recommender systems are widely deployed to provide user purchasing suggestion on eCommerce websites. The technology that has been adopted by most recommender systems is collaborative filtering. However, with the open nature of collaborative filtering recommender systems, they suffer significant vulnerabilities from being attacked by malicious raters, who inject profiles consisting of biased ratings. In recent years, several attack detection algorithms have been proposed to handle the issue. Unfortunately, their applications are restricted by various constraints. PCA-based methods while having good performance on paper, still suffer from missing values that plague most user–item matrixes. Classification-based methods require balanced numbers of attacks and normal profiles to train the classifiers. The detector based on SPC (Statistical Process Control) assumes that the rating probability distribution for each item is known in advance. In this research, Beta-Protection ( βP) is proposed to alleviate the problem without the abovementioned constraints. βP grounds its theoretical foundation on Beta distribution for easy computation and has stable performance when experimenting with data derived from the public websites of MovieLens
Keywords:	Shilling attacks detection Collaborative filtering Recommender systems
Author(s):	.
Source:	Decision Support Systems 55 (2013) 314–325
Subject:	تجارت الکترونیک
Category:	مقاله مجله
Release Date:	2013
No of Pages:	12
Price(Tomans):	0
بر اساس شرایط و ضوابط ارسال مقاله در سایت مدیر، این مطلب توسط یکی از نویسندگان ارسال گردیده است. در صورت مشاهده هرگونه تخلف، با تکمیل فرم گزارش تخلف حقوق مؤلفین مراتب را جهت پیگیری اطلاع دهید.

دریافت متن کامل
1.52 MB