insight-nb10.ipynb

{
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   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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   "source": [
    "best_als_model = ExplicitMF(ratings, n_factors=10, learning='als', \\\n",
    "                            item_fact_reg=0.1, user_fact_reg=0.1)\n",
    "best_als_model.train(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best_sgd_model = ExplicitMF(ratings, n_factors=80, learning='sgd', \\\n",
    "                            item_fact_reg=0.01, user_fact_reg=0.01, \\\n",
    "                            user_bias_reg=0.01, item_bias_reg=0.01)\n",
    "best_sgd_model.train(200, learning_rate=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cosine_similarity(model):\n",
    "    sim = model.item_vecs.dot(model.item_vecs.T)\n",
    "    norms = np.array([np.sqrt(np.diagonal(sim))])\n",
    "    return sim / norms / norms.T\n",
    "\n",
    "als_sim = cosine_similarity(best_als_model)\n",
    "sgd_sim = cosine_similarity(best_sgd_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load in movie data\n",
    "idx_to_movie = {}\n",
    "with open('u.item', 'r') as f:\n",
    "    for line in f.readlines():\n",
    "        info = line.split('|')\n",
    "        idx_to_movie[int(info[0])-1] = info[4]"
   ]
  }
 ],
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