Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Context has been recognized as an important factor to consider in personalized
Recommender Systems. However, most model-based Collaborative Filtering approaches …

Tagging plays an important role in many recent websites. Recommender systems can help
to suggest a user the tags he might want to use for tagging a specific item. Factorization …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Successive point-of-interest (POI) recommendation in location-based social networks
(LBSNs) becomes a significant task since it helps users to navigate a number of candidate …

Tensor-based methods have shown promise in improving upon traditional matrix
factorization methods for recommender systems. But tensors may achieve improved …

With the development of sophisticated e-learning environments, personalization is
becoming an important feature in e-learning systems due to the differences in background …

Incorporating phrase-level sentiment analysis on users' textual reviews for recommendation
has became a popular meth-od due to its explainable property for latent features and high …