A systematic literature review of sparsity issues in recommender systems
The tremendous expansion of information available on the web voraciously bombards
users, leaving them unable to make decisions and having no way of step** back to …
users, leaving them unable to make decisions and having no way of step** back to …
A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
A comprehensive survey on biclustering-based collaborative filtering
Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation
success is challenged by the diversity of user preferences, structural sparsity of user-item …
success is challenged by the diversity of user preferences, structural sparsity of user-item …
Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data
The recommender systems are recently becoming more significant in the age of rapid
development of the Internet technology due to their ability in making a decision to users on …
development of the Internet technology due to their ability in making a decision to users on …
EARS: Emotion-aware recommender system based on hybrid information fusion
Recommender systems suggest items that users might like according to their explicit and
implicit feedback information, such as ratings, reviews, and clicks. However, most …
implicit feedback information, such as ratings, reviews, and clicks. However, most …
A Novel K-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering
Data sparsity is a widespread problem of collaborative filtering (CF) recommendation
algorithms. However, some common CF methods cannot adequately utilize all user rating …
algorithms. However, some common CF methods cannot adequately utilize all user rating …
A survey on data mining techniques in recommender systems
Recommender systems have been regarded as gaining a more significant role with the
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …
Automated digital cause-and-effect diagrams to assist causal analysis in problem-solving: a data-driven approach
Causal analysis is an integral part of product quality problem-solving (QPS). Quality
management within the manufacturing industry has generated a considerable amount of …
management within the manufacturing industry has generated a considerable amount of …
A novel collaborative filtering recommendation approach based on soft co-clustering
M Li, L Wen, F Chen - Physica A: Statistical Mechanics and its Applications, 2021 - Elsevier
Collaborative Filtering (CF) recommendation algorithm has been widely applied into
recommender systems. Many CF algorithms associate a user/an item with one of subgroups …
recommender systems. Many CF algorithms associate a user/an item with one of subgroups …
Multi-view social recommendation via matrix factorization with sub-linear convergence rate
Abstract Recently Recommender Systems (RS) have become crucial tools to deal with
information retrieval and filtering in various applications, such as online business, social …
information retrieval and filtering in various applications, such as online business, social …