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Update fm_deep_dive.ipynb #1001

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Dec 5, 2019
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Update fm_deep_dive.ipynb #1001

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Update fm_deep_dive.ipynb
miguelgfierro Dec 5, 2019
4da8ee5
Update fm_deep_dive.ipynb
miguelgfierro Dec 5, 2019
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4 changes: 2 additions & 2 deletions notebooks/02_model/fm_deep_dive.ipynb
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Expand Up @@ -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, 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"
]
},
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