diff --git a/notebooks/elasticsearch-spark-recommender.ipynb b/notebooks/elasticsearch-spark-recommender.ipynb
index 57a5b9c..d3b6dc2 100644
--- a/notebooks/elasticsearch-spark-recommender.ipynb
+++ b/notebooks/elasticsearch-spark-recommender.ipynb
@@ -891,6 +891,21 @@
     "        hits = results['hits']['hits']\n",
     "        return src, hits[1:num+1]\n",
     "    \n",
+    "def get_similar_users(the_id, q=\"*\", num=10, index=\"demo\", dt=\"users\"):\n",
+    "    \"\"\"\n",
+    "    Given a user id, execute the recommendation function score query to find similar users, ranked by cosine similarity\n",
+    "    \"\"\"\n",
+    "    response = es.get(index=index, doc_type=dt, id=the_id)\n",
+    "    src = response['_source']\n",
+    "    if '@model' in src and 'factor' in src['@model']:\n",
+    "        raw_vec = src['@model']['factor']\n",
+    "        # our script actually uses the list form for the query vector and handles conversion internally\n",
+    "        query_vec = reverse_convert(raw_vec)\n",
+    "        q = fn_query(query_vec, q=q, cosine=True)\n",
+    "        results = es.search(index, dt, body=q, size=50)\n",
+    "        hits = results['hits']['hits']\n",
+    "        return src, hits[1:num+1]\n",
+    "    \n",
     "    \n",
     "def get_user_recs(the_id, q=\"*\", num=10, index=\"demo\"):\n",
     "    \"\"\"\n",
@@ -990,6 +1005,27 @@
     "        if i % 5 == 0:\n",
     "            sim_html += \"</tr><tr>\"\n",
     "    sim_html += \"</tr></table>\"\n",
+    "    display(HTML(sim_html))\n",
+    "    \n",
+    "def display_similar_users(the_id, q=\"*\", num=10, index=\"demo\", dt=\"users\"):\n",
+    "    \"\"\"\n",
+    "    Display similar users and similarity scores, in a table\n",
+    "    \"\"\"\n",
+    "    user, recs = get_similar_users(the_id, q, num, index, dt)\n",
+    "\n",
+    "    display(HTML(\"<h2>Get similar users for:</h2>\"))\n",
+    "    display(HTML(\"<h4>%s</h4>\" % user['id']))\n",
+    "    display(HTML(\"<br>\"))\n",
+    "    display(HTML(\"<h2>Similar users:</h2>\"))\n",
+    "    sim_html = \"<table border=0>\"\n",
+    "    i = 0\n",
+    "    for rec in recs:\n",
+    "        r_score = rec['_score']\n",
+    "        sim_html += \"<td><h5>%s</h5></td><td><h5>%2.3f</h5></td>\" % (rec['_id'], r_score)\n",
+    "        i += 1\n",
+    "        if i % 5 == 0:\n",
+    "            sim_html += \"</tr><tr>\"\n",
+    "    sim_html += \"</tr></table>\"\n",
     "    display(HTML(sim_html))"
    ]
   },
@@ -1068,9 +1104,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "### 5(b) Find movies to recommend to a user\n",
     "\n",
@@ -1114,6 +1148,24 @@
     "\n",
     "As you did with the similar movies recommendations, feel free to play around with the various queries you could pass into the user recommendation query."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5(c) Get users similar to another\n",
+    "\n",
+    "It is also possible to compute similar users for a given one. We have only indexed IDs for our users, but it is already functional enough."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_similar_users(31)"
+   ]
   }
  ],
  "metadata": {