From bb78a79fab0177bade3bf53b9212ec1da2ed7c6a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Miguel=20Gonz=C3=A1lez-Fierro?= <3491412+miguelgfierro@users.noreply.github.com> Date: Thu, 5 Dec 2019 11:14:28 +0000 Subject: [PATCH 1/2] Update fm_deep_dive.ipynb --- notebooks/02_model/fm_deep_dive.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/02_model/fm_deep_dive.ipynb b/notebooks/02_model/fm_deep_dive.ipynb index a90e43c3c7..923fe2d05f 100644 --- a/notebooks/02_model/fm_deep_dive.ipynb +++ b/notebooks/02_model/fm_deep_dive.ipynb @@ -45,8 +45,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For example, usually, the data that appears in recommendation problems can be encoded in the following way - the user, item, and feature vectors are transformed into one-hot representation for building model. For this arrangement, using the classic algorithms like linear regression, SVM, for predicting the target, are that\n", - "1. The feature vectors are highly sparse, and thus it makes it hard to converge the parameters to fit the model efficienly\n", + "In certain occasions, the data that appears in recommendation problems, such as user, item, and feature vectors, can be encoded into a one-hot representation. Under this arrangement, the use the classical algorithms like linear regression, SVM, have from the following problems:\n", + "1. The feature vectors are highly sparse, and thus it makes it hard to optimize the parameters to fit the model efficienly\n", "2. Cross-product of features will be sparse as well, and this in turn, reduces the expressiveness of a model if it is designed to capture the high-order interactions between features" ] }, From 4da8ee5b3c438f2ffbb47ee76f2ae0f13a3e2f14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Miguel=20Gonz=C3=A1lez-Fierro?= <3491412+miguelgfierro@users.noreply.github.com> Date: Thu, 5 Dec 2019 16:58:59 +0000 Subject: [PATCH 2/2] Update fm_deep_dive.ipynb --- notebooks/02_model/fm_deep_dive.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/02_model/fm_deep_dive.ipynb b/notebooks/02_model/fm_deep_dive.ipynb index 923fe2d05f..c2dcf5b0a9 100644 --- a/notebooks/02_model/fm_deep_dive.ipynb +++ b/notebooks/02_model/fm_deep_dive.ipynb @@ -45,7 +45,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In certain occasions, the data that appears in recommendation problems, such as user, item, and feature vectors, can be encoded into a one-hot representation. Under this arrangement, the use the classical algorithms like linear regression, SVM, have from the following problems:\n", + "In certain occasions, the data that appears in recommendation problems, such as user, item, and feature vectors, can be encoded into a one-hot representation. Under this arrangement, classical algorithms like linear regression and SVM may suffer from the following problems:\n", "1. The feature vectors are highly sparse, and thus it makes it hard to optimize the parameters to fit the model efficienly\n", "2. Cross-product of features will be sparse as well, and this in turn, reduces the expressiveness of a model if it is designed to capture the high-order interactions between features" ]