The huge amount of research papers on the web makes finding a relevant manuscript a
difficult task. In recent years many models were introduced to support researchers by …

Recommender systems (RS) are among the most widely used applications in data mining
and machine-learning technologies. These technologies recommend relevant products to …

In the era of social media, exponential growth of information generated by online social
media and e-commerce applications demands expert and intelligent recommendation …

Identifying and extracting user communities is an important step towards understanding
social network dynamics from a macro perspective. For this reason, the work in this paper …

The task of designing a recommender system is a complex process. Because of the many
technological advancements that may be included in a recommender system, engineers are …

Recommender systems have been used since the beginning of the Web to assist users with
personalized suggestions related to past preferences for items or products including books …

Recommender systems are challenging research problems being exploited to suggest new
items or services, such as books, music and movies, and even people, to users based on …

This paper constructs a novel enhanced latent semantic model based on users' comments,
and employs regularization factors to capture the temporal evolution characteristics of users' …

Tensor factorization has been applied in recommender systems to discover latent factors
between multidimensional data such as time, place, and social context. However, tensor …

Influencer marketing is emerging as a new marketing method, changing the marketing
strategies of brands profoundly. In order to help brands find suitable micro-influencers as …