Network representation learning: a systematic literature review
B Li, D Pi - Neural Computing and Applications, 2020 - Springer
Omnipresent network/graph data generally have the characteristics of nonlinearity,
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …
[图书][B] Recommender systems
CC Aggarwal - 2016 - Springer
“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 …
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
Leveraging social connections to improve personalized ranking for collaborative filtering
Recommending products to users means estimating their preferences for certain items over
others. This can be cast either as a problem of estimating the rating that each user will give …
others. This can be cast either as a problem of estimating the rating that each user will give …
BPR: Bayesian personalized ranking from implicit feedback
Item recommendation is the task of predicting a personalized ranking on a set of items (eg
websites, movies, products). In this paper, we investigate the most common scenario with …
websites, movies, products). In this paper, we investigate the most common scenario with …
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
Context has been recognized as an important factor to consider in personalized
Recommender Systems. However, most model-based Collaborative Filtering approaches …
Recommender Systems. However, most model-based Collaborative Filtering approaches …
On sampling strategies for neural network-based collaborative filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction information …
recommendation algorithms that can incorporate both (1) user-item interaction information …
RDF2Vec: RDF graph embeddings and their applications
Abstract Linked Open Data has been recognized as a valuable source for background
information in many data mining and information retrieval tasks. However, most of the …
information in many data mining and information retrieval tasks. However, most of the …
MyMediaLite: A free recommender system library
MyMediaLite is a fast and scalable, multi-purpose library of recommender system
algorithms, aimed both at recommender system researchers and practitioners. It addresses …
algorithms, aimed both at recommender system researchers and practitioners. It addresses …
An accelerated gradient method for trace norm minimization
We consider the minimization of a smooth loss function regularized by the trace norm of the
matrix variable. Such formulation finds applications in many machine learning tasks …
matrix variable. Such formulation finds applications in many machine learning tasks …
Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis
The popularity bias problem is one of the most prominent challenges of recommender
systems, ie, while a few heavily rated items receive much attention in presented …
systems, ie, while a few heavily rated items receive much attention in presented …