مقاله انگلیسی رایگان در مورد یک رویکرد فازی برای مدیریت نویز طبیعی – الزویر 2018

Introduction

The Web allows people accessing to a huge amount of information. However, the users skills to cope with all the available information are limited, which leads to select suboptimal alternatives. This problem is known as information overload. Recommender Systems (RSs) are tools to help individuals to overcome such information overload problem personalizing access to information (Adomavicius & Tuzhilin, 2005; Ekstrand, Riedl, & Konstan, 2011). However, some items tend to be consumed by groups of users, such as tourist attractions (Garcia, Pajares, Sebastia, & Onaindia, 2012) or television programmes (Said, Berkovsky, & De Luca, 2011). With this purpose in mind, Group Recommender Systems (GRSs) (Masthoff, 2015) help groups of users to find suitable items according to their preferences and needs. Several techniques have been used to improve individual recommendation, such as neighborhood-based collaborative filtering (Sarwar, Karypis, Konstan, & Riedl, 2001), matrix factorisation (Koren, Bell, & Volinsky, 2009), or approaches that consider temporal dynamics (Koren, 2010; Rafailidis, Kefalas, & Manolopoulos, 2017). In the case of group recommendation, there are approaches to aggregate individual information (Masthoff, 2015), to consider consensus among members (Castro, Quesada, Palomares, & Martínez, 2015), or matrix factorisation models for groups (Ortega, Hernando, Bobadilla, & Kang, 2016). A decade ago, it was pointed out that explicitly stated user preferences may not be error free (O’Mahony, Hurley, & Silvestre, 2006). More recently, other recent works (Bellogín, Said, & de Vries, 2014; Centeno, Hermoso, & Fasli, 2015; Guo & Dunson, 2015; Zhang, Zhao, & Lui, 2017) have also pointed out that a person’s ratings are noisy, inconsistent, and biased. Li, Chen, Zhu, and Zhang (2013) determined that too many noisy ratings can distort users’ preference profiles, which result in unlike-minded neighbors that imply a quality loss in recommendations. Kluver, Nguyen, Ekstrand, Sen, and Riedl (2012) have also suggested that user ratings are imperfect and noisy, and such noise limits the predictive power of any RS. Therefore, in addition to improving recommendations through new recommendation approaches, researchers should also focus on improving the quality of the rating database (Amatriain, Pujol, Tintarev, & Oliver, 2009c). In RSs, there are two kinds of noise in the database (O’Mahony et al., 2006): (i) malicious noise, that consists of erroneous data deliberately inserted in the system to influence recommendations, and (ii) natural noise, that appears when users unpurposely introduce erroneous data due to human errors or external factors during the rating process. This paper focuses on the latter. Natural noise biases recommendations, therefore, its management is a key factor to improve them. There are several Natural Noise Management (NNM) approaches for individual RSs databases. While some NNM approaches need additional information (Amatriain, Lathia, Pujol, Kwak, & Oliver, 2009a; Pham & Jung, 2013), others detect and correct the natural noise using information already contained in the database (Yera, Castro, & Martínez, 2016; Yera Toledo, Caballero Mota, & Martínez, 2015). GRSs also rely on databases with explicit users’ preferences (Masthoff, 2015), therefore, they are affected by natural noise. Castro, Yera, and Martínez (2017) propose a NNM approach for GRSs to manage ratings and noise using crisp values. This is the only work focused on NNM in GRSs.