A literature review and classification of recommender systems research
DH Park, HK Kim, IY Choi, JK Kim - Expert systems with applications, 2012 - Elsevier
Recommender systems have become an important research field since the emergence of
the first paper on collaborative filtering in the mid-1990s. Although academic research on …
the first paper on collaborative filtering in the mid-1990s. Although academic research on …
Recommender systems survey
Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …
on demographic, content-based and collaborative filtering. Currently, these systems are …
A collaborative filtering approach to mitigate the new user cold start problem
The new user cold start issue represents a serious problem in recommender systems as it
can lead to the loss of new users who decide to stop using the system due to the lack of …
can lead to the loss of new users who decide to stop using the system due to the lack of …
A new collaborative filtering metric that improves the behavior of recommender systems
J Bobadilla, F Serradilla, J Bernal - Knowledge-Based Systems, 2010 - Elsevier
Recommender systems are typically provided as Web 2.0 services and are part of the range
of applications that give support to large-scale social networks, enabling on-line …
of applications that give support to large-scale social networks, enabling on-line …
A collaborative location based travel recommendation system through enhanced rating prediction for the group of users
L Ravi, S Vairavasundaram - Computational intelligence and …, 2016 - Wiley Online Library
Rapid growth of web and its applications has created a colossal importance for
recommender systems. Being applied in various domains, recommender systems were …
recommender systems. Being applied in various domains, recommender systems were …
Incremental collaborative filtering recommender based on regularized matrix factorization
X Luo, Y Xia, Q Zhu - Knowledge-Based Systems, 2012 - Elsevier
The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …
A collaborative filtering similarity measure based on singularities
Recommender systems play an important role in reducing the negative impact of information
overload on those websites where users have the possibility of voting for their preferences …
overload on those websites where users have the possibility of voting for their preferences …
Multi-adjoint t-concept lattices
J Medina, M Ojeda-Aciego - Information Sciences, 2010 - Elsevier
The t-concept lattice is introduced as a set of triples associated to graded tabular information
interpreted in a non-commutative fuzzy logic. Following the general techniques of formal …
interpreted in a non-commutative fuzzy logic. Following the general techniques of formal …
A hybrid recommendation technique based on product category attributes
A Albadvi, M Shahbazi - Expert Systems with Applications, 2009 - Elsevier
Recommender systems are powerful tools that allow companies to present personalized
offers to their customers and defined as a system which recommends an appropriate product …
offers to their customers and defined as a system which recommends an appropriate product …
An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems
Matrix-Factorization (MF) is an accurate and scalable approach for collaborative filtering
(CF)-based recommender systems. The performance of matrix MF methods depends on how …
(CF)-based recommender systems. The performance of matrix MF methods depends on how …