Search Word(s) in Title, Keywords, Authors, and Abstract: Recommender Systems

Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries
Carlos Porcel, Alberto Ching-López, Juan Bernabé-Moreno, Alvaro Tejeda-Lorente and Enrique Herrera-Viedma
Page: 653~667, Vol. 13, No.4, 2017
10.3745/JIPS.04.0035

Keywords: Digital Libraries, Dissemination of Information, Fuzzy Linguistic Modeling, Recommender Systems
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The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user’s access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user’s preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.


Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering
Woon-hae Jeong, Se-jun Kim, Doo-soon Park and Jin Kwak
Page: 157~172, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.157
Keywords: Collaborative Filtering, Movie Recommendation System, Personal Propensity, Security, Push Stack
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There are many recommendation systems available to provide users with personalized services. Among them, the most frequently used in electronic commerce is "'"collaborative filtering"'", which is a technique that provides a process of filtering customer information for the preparation of profiles and making recommendations of products that are expected to be preferred by other users, based on such information profiles. Collaborative filtering systems, however, have in their nature both technical issues such as sparsity, scalability, and transparency, as well as security issues in the collection of the information that becomes the basis for preparation of the profiles. In this paper, we suggest a movie recommendation system, based on the selection of optimal personal propensity variables and the utilization of a secure collaborating filtering system, in order to provide a solution to such sparsity and scalability issues. At the same time,we adopt "'"push attack"'" principles to deal with the security vulnerability of collaborative filtering systems. Furthermore, we assess the system"'"s applicability by using the open database MovieLens, and present a personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the selection of optimal personalization factors and the embodiment of a safe collaborative filtering system


A Regularity-Based Preprocessing Method for Collaborative Recommender Systems
Raciel Yera Toledo, Yailé Caballero Mota and Milton García Borroto
Page: 435~460, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.435
Keywords: Collaborative Recommender Systems, Inconsistencies, Rating Regularities
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Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.


An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators
B. John Oommen, Anis Yazidi and Ole-Christoffer Granmo
Page: 191~212, Vol. 8, No.2, 2012
10.3745/JIPS.2012.8.2.191
Keywords: Weak es timators, User's Profiling, Time Varying Preferences
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Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user"'"s interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning” capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user"'"s preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user"'"s time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.