A quality based recommender system to disseminate information in a university digital library

Tejeda-Lorente, Álvaro; Porcel, Carlos; Peis, Eduardo; Sanz, Rosa Lorés; Herrera‐Viedma, Enrique

doi:10.1016/j.ins.2013.10.036

Cited by 157 publications

(76 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…--Fuzzy linguistic scales These scales are frequently considered for different goals as an a posteriori tool to encode data from a discrete (often a Likert) scale by means of fuzzy numbers (see, for instance, Zadeh 1975a, b, c;Tong and Bonissone 1980;Pedrycz 1989;Herrera et al 1998Herrera et al , 2008Lalla et al 2008;Li 2013;Akdag et al 2014;Estrella et al 2014;Massanet et al 2014;Tejeda-Lorente et al 2014Villacorta et al 2014;Wang et al 2014;Garcia-Galán et al 2015;Liu et al 2015a;Tavana et al 2015). --Fuzzy rating scale This scale has been introduced by Hesketh et al (1988).…”

Section: Fuzzy Data: Fuzzy Representation Of Linguistic Terms Ordinamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

View full text Add to dashboard Cite

Section: Fuzzy Data: Fuzzy Representation Of Linguistic Terms Ordinamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

View full text Add to dashboard Cite

“…Based on the research carried out in paper [19]  Purpose -The compelling reason for implementing recommendations in E-commerce domain is that they have turned out to be serious business tools to enhance the sales by improving cross-sell by suggesting additional products and gaining customer loyalty resulting in repeat business [20]. In university digital library, recommender system is proposed to disseminate information based on quality to help users access relevant research resources among the thousands of resources that are available but yet hard to find [21], [22]. Recommendation systems are highly vulnerable to external manipulations especially in E-commerce where rating biasness can be introduced by companies who wish to recommend their products more than their competitors (Shilling attacks).…”

Section: Dimensions Of Recommender Systemmentioning

confidence: 99%

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Rastogi¹,

Singh²

2016

ijacsa

View full text Add to dashboard Cite

Abstract-The issue of information overload could be effectively managed with the help of intelligent system which is capable of proactively supervising the users in accessing relevant or useful information in a tailored way, by pruning the large space of possible options. But the key challenge lies in what all information can be collected and assimilated to make effective recommendations. This paper discusses reasons for evolution of recommender systems leading to transition from traditional to social information based recommendations. Social Recommender System (SRS) exploits social contextual information in the form of social links of users, social tags, user-generated data that contain huge supplemental information about items or services that are expected to be of interest of user or about features of items. Therefore, having tremendous potential for improving recommendation quality. Systematic literature review has been done for SRS by categorizing various kinds of social-contextual information into explicit and implicit user-item information. This paper also analyses key aspects of any generic recommender system namely Domain, Personalization Levels, Privacy and Trustworthiness, Recommender algorithms to give a better understanding to researchers new in this field.

show abstract

“…The system's ability to gather information has been enhanced, and recommendation systems based on contextual modeling approach have become popular [1][2][3][4]. Most systems require to analyze and manage a large amount of large volume data such as Web and social contextual information over multidimensional.…”

Section: Introductionmentioning

confidence: 99%

MRTensorCube: tensor factorization with data reduction for context-aware recommendations

et al. 2017

View full text Add to dashboard Cite

Context information can be an important factor of user behavior modeling and various context recognition recommendations. However, state-of-the-art context modeling methods cannot deal with contexts of other dimensions such as those of users and items and cannot extract special semantics. On the other hand, some tasks for predicting multidimensional relationships can be used to recommend context recognition, but there is a problem with the generation recommendations based on a variety of context information. In this paper, we propose MRTensorCube, which is a largescale data cube calculation based on distributed parallel computing using MapReduce computation framework and supports efficient context recognition. The basic idea of MRTensorCube is the reduction of continuous data combined partial filter and slice when calculating using a four-way algorithm. From the experimental results, it is clear that MRTensor is superior to all other algorithms.

show abstract

A quality based recommender system to disseminate information in a university digital library

Cited by 157 publications

References 42 publications

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

MRTensorCube: tensor factorization with data reduction for context-aware recommendations

Contact Info

Product

Resources

About