Variational Autoencoders for Collaborative Filtering

16 Feb 2018Dawen LiangRahul G. KrishnanMatthew D. HoffmanTony Jebara

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Recommendation Systems Million Song Dataset Mult-DAE [email protected] 0.266 # 4
[email protected] 0.363 # 4
[email protected] 0.313 # 5
Recommendation Systems Million Song Dataset Mult-VAE PR [email protected] 0.266 # 4
[email protected] 0.364 # 3
[email protected] 0.316 # 4
Recommendation Systems MovieLens 20M Mult-DAE [email protected] 0.387 # 6
[email protected] 0.524 # 5
[email protected] 0.419 # 6
Recommendation Systems MovieLens 20M Mult-VAE PR [email protected] 0.395 # 4
[email protected] 0.537 # 4
[email protected] 0.426 # 4
Recommendation Systems Netflix Mult-VAE PR [email protected] 0.351 # 5
[email protected] 0.444 # 5
[email protected] 0.386 # 5
Recommendation Systems Netflix Mult-DAE [email protected] 0.344 # 6
[email protected] 0.438 # 6
[email protected] 0.380 # 6

Methods used in the Paper


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