Coupled item-based matrix factorization
The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that
the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF) …
the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF) …
FeatureMF: an item feature enriched matrix factorization model for item recommendation
Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF)
techniques used in recommender systems due to its effectiveness and ability to deal with …
techniques used in recommender systems due to its effectiveness and ability to deal with …
Attributes coupling based matrix factorization for item recommendation
Recommender systems have attracted lots of attention since they alleviate the information
overload problem for users. Matrix factorization is one of the most widely employed …
overload problem for users. Matrix factorization is one of the most widely employed …
Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering
Recommendation techniques play a vital role in recommending an actual product to an
intended user. The recommendation also supports the user in the decision-making process …
intended user. The recommendation also supports the user in the decision-making process …
Attributes coupling based item enhanced matrix factorization technique for recommender systems
Recommender system has attracted lots of attentions since it helps users alleviate the
information overload problem. Matrix factorization technique is one of the most widely …
information overload problem. Matrix factorization technique is one of the most widely …
Exploiting implicit item relationships for recommender systems
Collaborative filtering inherently suffers from the data sparsity and cold start problems.
Social networks have been shown useful to help alleviate these issues. However, social …
Social networks have been shown useful to help alleviate these issues. However, social …
A neighborhood-based matrix factorization technique for recommendation
M Guo, J Sun, X Meng - Annals of Data Science, 2015 - Springer
The data sparsity and prediction quality are recognized as the key challenges in the existing
recommender Systems. Most of the existing recommender systems depend on collaborating …
recommender Systems. Most of the existing recommender systems depend on collaborating …
Matrix factorization meets cosine similarity: addressing sparsity problem in collaborative filtering recommender system
H Wen, G Ding, C Liu, J Wang - … and Applications: 16th Asia-Pacific Web …, 2014 - Springer
Matrix factorization (MF) technique has been widely used in collaborative filtering
recommendation systems. However, MF still suffers from data sparsity problem. Although …
recommendation systems. However, MF still suffers from data sparsity problem. Although …
Incorporating hierarchical information into the matrix factorization model for collaborative filtering
A Mashhoori, S Hashemi - … Information and Database Systems: 4th Asian …, 2012 - Springer
Matrix factorization (MF) is one of the well-known methods in collaborative filtering to build
accurate and efficient recommender systems. While in all the previous studies about MF …
accurate and efficient recommender systems. While in all the previous studies about MF …
Multi-linear interactive matrix factorization
Recommender systems, which can significantly help users find their interested items from
the information era, has attracted an increasing attention from both the scientific and …
the information era, has attracted an increasing attention from both the scientific and …