From 1da9670ed778ad756755e77006fd30123f4abd4f Mon Sep 17 00:00:00 2001 From: Martin Date: Sun, 26 May 2024 13:53:23 +0800 Subject: [PATCH] Update cornac_bivae_deep_dive.ipynb: fix typos --- .../cornac_bivae_deep_dive.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb b/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb index 731ab0c12..fb432ccca 100644 --- a/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb +++ b/examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb @@ -610,7 +610,7 @@ "source": [ "## 4 Discussion\n", "\n", - "BiVAE is a new variational autoencoder tailored for dyadic data, where observations consist of measurements associated with two sets of objects, e.g., users, items and corresponding ratings. The model is symmetric, which makes it easier to extend axiliary data from both sides of users and items. In addition to preference data, the model can be applied to other types of dyadic data such as documentword matrices, and other tasks such as co-clustering. \n", + "BiVAE is a new variational autoencoder tailored for dyadic data, where observations consist of measurements associated with two sets of objects, e.g., users, items and corresponding ratings. The model is symmetric, which makes it easier to extend auxiliary data from both sides of users and items. In addition to preference data, the model can be applied to other types of dyadic data such as document-word matrices, and other tasks such as co-clustering. \n", "\n", "In the paper, there is also a discussion on Constrained Adaptive Priors (CAP), a proposed method to build informative priors to mitigate the well-known posterior collapse problem. We have left out that part purposely, not to distract the audiences. Nevertheless, it is very interesting and worth taking a look. \n", "\n",