While recommendation plays an increasingly critical role in our living, study, work, and
entertainment, the recommendations we receive are often for irrelevant, duplicate, or …

Complex applications such as big data analytics involve different forms of coupling
relationships that reflect interactions between factors related to technical, business (domain …

Most of the classic theoretical systems and tools in statistics, data mining and machine
learning are built on the fundamental assumption of IIDness, which assumes the …

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recommender system discloses the nature of recommendation and has shown its potential …

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 …

Currently, most of the existing recommendation methods treat social network users equally,
which assume that the effect of recommendation on a user is decided by the user's own …

The factorization machine models attract significant attention nowadays since they improve
recommendation performance by incorporating context information into recommendation …

Many existing recommendation methods such as matrix factorization (MF) mainly rely on
user–item rating matrix, which sometimes is not informative enough, often suffering from the …

Recommendation systems have been well established to reduce the problem of information
overload and have become one of the most valuable tools applicable to different domains …

Classic collaborative filtering methods suffer from lack of accuracy when it comes to todays
complicated ecommerce websites. Recommender systems can be more efficient and …