Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges
Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …
developing algorithms that generate recommendations. The resulting research progress has …
Rating-based collaborative filtering: algorithms and evaluation
Recommender systems help users find information by recommending content that a user
might not know about, but will hopefully like. Rating-based collaborative filtering …
might not know about, but will hopefully like. Rating-based collaborative filtering …
Collaborative filtering and deep learning based recommendation system for cold start items
Recommender system is a specific type of intelligent systems, which exploits historical user
ratings on items and/or auxiliary information to make recommendations on items to the …
ratings on items and/or auxiliary information to make recommendations on items to the …
Collaborative topic modeling for recommending scientific articles
Researchers have access to large online archives of scientific articles. As a consequence,
finding relevant papers has become more difficult. Newly formed online communities of …
finding relevant papers has become more difficult. Newly formed online communities of …
Improving content-based and hybrid music recommendation using deep learning
Existing content-based music recommendation systems typically employ a\textit {two-stage}
approach. They first extract traditional audio content features such as Mel-frequency cepstral …
approach. They first extract traditional audio content features such as Mel-frequency cepstral …
Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence
Matrix factorization (MF) models and their extensions are standard in modern recommender
systems. MF models decompose the observed user-item interaction matrix into user and …
systems. MF models decompose the observed user-item interaction matrix into user and …
Bilateral variational autoencoder for collaborative filtering
Preference data is a form of dyadic data, with measurements associated with pairs of
elements arising from two discrete sets of objects. These are users and items, as well as …
elements arising from two discrete sets of objects. These are users and items, as well as …
Next-term student performance prediction: A recommender systems approach
An enduring issue in higher education is student retention to successful graduation. National
statistics indicate that most higher education institutions have four-year degree completion …
statistics indicate that most higher education institutions have four-year degree completion …
Content-based recommendations with Poisson factorization
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles
and reader preferences. CTPF can be used to build recommender systems by learning from …
and reader preferences. CTPF can be used to build recommender systems by learning from …
Co-factorization machines: modeling user interests and predicting individual decisions in twitter
L Hong, AS Doumith, BD Davison - … conference on Web search and data …, 2013 - dl.acm.org
Users of popular services like Twitter and Facebook are often simultaneously overwhelmed
with the amount of information delivered via their social connections and miss out on much …
with the amount of information delivered via their social connections and miss out on much …