Research commentary on recommendations with side information: A survey and research directions
Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …
overload problem in recent decades. Traditional recommender systems, however, suffer …
Scholarly paper recommendation via user's recent research interests
K Sugiyama, MY Kan - Proceedings of the 10th annual joint conference …, 2010 - dl.acm.org
We examine the effect of modeling a researcher's past works in recommending scholarly
papers to the researcher. Our hypothesis is that an author's published works constitute a …
papers to the researcher. Our hypothesis is that an author's published works constitute a …
Where to go next: A spatio-temporal gated network for next poi recommendation
Next Point-of-Interest (POI) recommendation which is of great value to both users and POI
holders is a challenging task since complex sequential patterns and rich contexts are …
holders is a challenging task since complex sequential patterns and rich contexts are …
Recommender systems survey
Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …
on demographic, content-based and collaborative filtering. Currently, these systems are …
A contextual-bandit approach to personalized news article recommendation
Personalized web services strive to adapt their services (advertisements, news articles, etc.)
to individual users by making use of both content and user information. Despite a few recent …
to individual users by making use of both content and user information. Despite a few recent …
A collaborative filtering approach to mitigate the new user cold start problem
The new user cold start issue represents a serious problem in recommender systems as it
can lead to the loss of new users who decide to stop using the system due to the lack of …
can lead to the loss of new users who decide to stop using the system due to the lack of …
Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings
Click-through rate (CTR) prediction has been one of the most central problems in
computational advertising. Lately, embedding techniques that produce low-dimensional …
computational advertising. Lately, embedding techniques that produce low-dimensional …
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
HJ Ahn - Information sciences, 2008 - Elsevier
Collaborative filtering is one of the most successful and widely used methods of automated
product recommendation in online stores. The most critical component of the method is the …
product recommendation in online stores. The most critical component of the method is the …
Regression-based latent factor models
D Agarwal, BC Chen - Proceedings of the 15th ACM SIGKDD …, 2009 - dl.acm.org
We propose a novel latent factor model to accurately predict response for large scale dyadic
data in the presence of features. Our approach is based on a model that predicts response …
data in the presence of features. Our approach is based on a model that predicts response …
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks
Recommender systems enable users to access products or articles that they would
otherwise not be aware of due to the wealth of information to be found on the Internet. The …
otherwise not be aware of due to the wealth of information to be found on the Internet. The …