The paper supplies a taxonomy that maps deep learning techniques to cold start (via extra features and hidden representations) and candidate generation (via separate networks, RNNs, autoencoders, and hybrids) challenges in recommender systems.
The advancement of several frameworks like PyTorch from Facebook (Ketkar, 2017), MXNet from Apache (Chen et al
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research Survey
The paper supplies a taxonomy that maps deep learning techniques to cold start (via extra features and hidden representations) and candidate generation (via separate networks, RNNs, autoencoders, and hybrids) challenges in recommender systems.