Search Results for author:
Found 2 papers, 1 papers with code
Attribute-aware non-linear co-embeddings of graph features
In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings.
Ranked #4 on
Recommendation Systems
on MovieLens 100K
(using extra training data)
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings
The task of classifying multi-relational data spans a wide range of domains such as document classification in citation networks, classification of emails, and protein labeling in proteins interaction graphs.
