Context-aware collaborative topic regression with social matrix factorization for recommender systems
Online social networking sites have become popular platforms on which users can link with
each other and share information, not only basic rating information but also information such …
each other and share information, not only basic rating information but also information such …
Relational collaborative topic regression for recommender systems
Due to its successful application in recommender systems, collaborative filtering (CF) has
become a hot research topic in data mining and information retrieval. In traditional CF …
become a hot research topic in data mining and information retrieval. In traditional CF …
Collaborative topic regression with social matrix factorization for recommendation systems
Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular
platform for users to connect with each other and share content or opinions. They provide …
platform for users to connect with each other and share content or opinions. They provide …
Collaborative recommendation with user generated content
In the age of Web 2.0, user generated content (UGC), such as user review and social tag,
ubiquitously exists on the Internet. Although there exist different kinds of UGC in …
ubiquitously exists on the Internet. Although there exist different kinds of UGC in …
Collaborative topic regression with social trust ensemble for recommendation in social media systems
Social media systems provide ever-growing huge volumes of information for dissemination
and communication among communities of users, while recommender systems aim to …
and communication among communities of users, while recommender systems aim to …
Leveraging implicit relations for recommender systems
A Li, B Yang, H Huo, FK Hussain - Information Sciences, 2021 - Elsevier
Collaborative filtering (CF) is one of the dominant techniques used in recommender
systems. Most CF-based methods treat every user (or item) as an isolated existence, without …
systems. Most CF-based methods treat every user (or item) as an isolated existence, without …
Improving recommender systems by incorporating social contextual information
Due to their potential commercial value and the associated great research challenges,
recommender systems have been extensively studied by both academia and industry …
recommender systems have been extensively studied by both academia and industry …
Improving matrix approximation for recommendation via a clustering-based reconstructive method
K Ji, R Sun, X Li, W Shu - Neurocomputing, 2016 - Elsevier
Matrix approximation is a common model-based approach to collaborative filtering in
recommender systems. Many relevant algorithms that fuse social contextual information …
recommender systems. Many relevant algorithms that fuse social contextual information …
Hybrid collaborative recommendation via semi-autoencoder
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We
generalize it into a hybrid collaborative filtering model for rating prediction as well as …
generalize it into a hybrid collaborative filtering model for rating prediction as well as …
Collaborative topic regression for online recommender systems: an online and Bayesian approach
Abstract Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix
factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has …
factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has …