“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 …

Conventional methods of matrix completion are linear methods that are not effective in
handling data of nonlinear structures. Recently a few researchers attempted to incorporate …

Restaurant recommendation is one of the most interesting recommendation problems
because of its high practicality and rich context. Many works have been proposed to …

A recommender system (RS) is a data filtering technique that suggests the appropriate
information to the end-user. Collaborative filtering is the most frequently deployed algorithm …

Model-Based Collaborative Filtering | SpringerLink Skip to main content Advertisement
SpringerLink Account Menu Find a journal Publish with us Track your research Search Cart …

The tremendous growth in information over the last decade leads to information
overwhelming problems for accessing personalized products. The recommender framework …

In the collaborative filtering (CF) recommendation applications, the sparsity of user rating
data, the effectiveness of cold start, the strategy of item information neglection, and user …

The Collaborative Filtering (CF) recommendation algorithm, one of the most popular
algorithms in Recommendation Systems (RS), mainly includes memory-based and model …

Conventional matrix completion methods are generally linear because they assume that the
given data are from linear transformations of lower-dimensional latent subspace and the …

Heterogeneous domain adaptation (HDA) aims to solve the learning problems where the
source-and the target-domain data are represented by heterogeneous types of features. The …