64 citations · 285 across the 26 of their papers we have counts for
Showing 2018 · stat.MLShow all
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stat.ML2018
Item Recommendation with Variational Autoencoders and Heterogenous Priors
Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan +3
In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation o…
stat.ML2018★ 64 cited
Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman +1
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacit…