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captain
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add preprocess data
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ml-1m/0.2/test_item.dat

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ml-1m/0.2/test_user.dat

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ml-1m/0.2/train_item.dat

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ml-1m/0.2/train_user.dat

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ml-1m/0.2/valid_item.dat

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ml-1m/0.2/valid_user.dat

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ml-1m/Untitled.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pickle\n",
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"\n",
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"R = pickle.load(open('ratings.all','rb'))\n",
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"D_all = pickle.load(open('document.all','rb'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"8000"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(D_all['X_vocab'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

ml-1m/document.all

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ml-1m/ratings.all

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