Thirteenth ACM Conference on Recommender Systems (RecSys ’19) 2019

Attribute-aware non-linear co-embeddings of graph features

Thirteenth ACM Conference on Recommender Systems (RecSys ’19) 2019 ahmedrashed-ml/GraphRec

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.

RECOMMENDATION SYSTEMS