diff --git a/data-science/model/recsys.ipynb b/data-science/model/recsys.ipynb
new file mode 100644
index 0000000..577f67c
--- /dev/null
+++ b/data-science/model/recsys.ipynb
@@ -0,0 +1,600 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "382f9667-f39c-4766-9f56-ddf936941cec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# импортируем необходимые библиотеки\n",
+    "import pandas as pd\n",
+    "from scipy.sparse import csr_matrix\n",
+    "from implicit.cpu.als import AlternatingLeastSquares\n",
+    "from implicit.evaluation import mean_average_precision_at_k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "474de742-7219-4777-9afa-8ae2b268aa45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('interactions.csv', parse_dates=['last_watch_dt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c712c399-8c1f-4394-a03a-97a19cbacd2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>last_watch_dt</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>watched_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-05-11</td>\n",
+       "      <td>4250</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699317</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>2021-05-29</td>\n",
+       "      <td>8317</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>656683</td>\n",
+       "      <td>7107</td>\n",
+       "      <td>2021-05-09</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>864613</td>\n",
+       "      <td>7638</td>\n",
+       "      <td>2021-07-05</td>\n",
+       "      <td>14483</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>964868</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-04-30</td>\n",
+       "      <td>6725</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476246</th>\n",
+       "      <td>648596</td>\n",
+       "      <td>12225</td>\n",
+       "      <td>2021-08-13</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476247</th>\n",
+       "      <td>546862</td>\n",
+       "      <td>9673</td>\n",
+       "      <td>2021-04-13</td>\n",
+       "      <td>2308</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476248</th>\n",
+       "      <td>697262</td>\n",
+       "      <td>15297</td>\n",
+       "      <td>2021-08-20</td>\n",
+       "      <td>18307</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476249</th>\n",
+       "      <td>384202</td>\n",
+       "      <td>16197</td>\n",
+       "      <td>2021-04-19</td>\n",
+       "      <td>6203</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476250</th>\n",
+       "      <td>319709</td>\n",
+       "      <td>4436</td>\n",
+       "      <td>2021-08-15</td>\n",
+       "      <td>3921</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5476251 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         user_id  item_id last_watch_dt  total_dur  watched_pct\n",
+       "0         176549     9506    2021-05-11       4250         72.0\n",
+       "1         699317     1659    2021-05-29       8317        100.0\n",
+       "2         656683     7107    2021-05-09         10          0.0\n",
+       "3         864613     7638    2021-07-05      14483        100.0\n",
+       "4         964868     9506    2021-04-30       6725        100.0\n",
+       "...          ...      ...           ...        ...          ...\n",
+       "5476246   648596    12225    2021-08-13         76          0.0\n",
+       "5476247   546862     9673    2021-04-13       2308         49.0\n",
+       "5476248   697262    15297    2021-08-20      18307         63.0\n",
+       "5476249   384202    16197    2021-04-19       6203        100.0\n",
+       "5476250   319709     4436    2021-08-15       3921         45.0\n",
+       "\n",
+       "[5476251 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5af68c1f-a8be-46c2-ab70-4cbec16284a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5476251 entries, 0 to 5476250\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column         Non-Null Count    Dtype         \n",
+      "---  ------         --------------    -----         \n",
+      " 0   user_id        5476251 non-null  int64         \n",
+      " 1   item_id        5476251 non-null  int64         \n",
+      " 2   last_watch_dt  5476251 non-null  datetime64[ns]\n",
+      " 3   total_dur      5476251 non-null  int64         \n",
+      " 4   watched_pct    5475423 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(1), int64(3)\n",
+      "memory usage: 208.9 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info(show_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2271d643-6004-4b77-814e-6fe0f16e4e69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['watched_pct'] = df['watched_pct'].fillna(0) #заполним пропущенные значения нулями"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "510bf1f1-28b4-442e-8dbe-f714ec2ea43f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2021-03-13 00:00:00'), Timestamp('2021-08-22 00:00:00'))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['last_watch_dt'].min(), df['last_watch_dt'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cb768800-08a8-4e77-816f-ca6f7b719f0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>last_watch_dt</th>\n",
+       "      <th>total_dur</th>\n",
+       "      <th>watched_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>176549</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-05-11</td>\n",
+       "      <td>4250</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>699317</td>\n",
+       "      <td>1659</td>\n",
+       "      <td>2021-05-29</td>\n",
+       "      <td>8317</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>656683</td>\n",
+       "      <td>7107</td>\n",
+       "      <td>2021-05-09</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>864613</td>\n",
+       "      <td>7638</td>\n",
+       "      <td>2021-07-05</td>\n",
+       "      <td>14483</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>964868</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>2021-04-30</td>\n",
+       "      <td>6725</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476245</th>\n",
+       "      <td>786732</td>\n",
+       "      <td>4880</td>\n",
+       "      <td>2021-05-12</td>\n",
+       "      <td>753</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476246</th>\n",
+       "      <td>648596</td>\n",
+       "      <td>12225</td>\n",
+       "      <td>2021-08-13</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476247</th>\n",
+       "      <td>546862</td>\n",
+       "      <td>9673</td>\n",
+       "      <td>2021-04-13</td>\n",
+       "      <td>2308</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476249</th>\n",
+       "      <td>384202</td>\n",
+       "      <td>16197</td>\n",
+       "      <td>2021-04-19</td>\n",
+       "      <td>6203</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5476250</th>\n",
+       "      <td>319709</td>\n",
+       "      <td>4436</td>\n",
+       "      <td>2021-08-15</td>\n",
+       "      <td>3921</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5051815 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         user_id  item_id last_watch_dt  total_dur  watched_pct\n",
+       "0         176549     9506    2021-05-11       4250         72.0\n",
+       "1         699317     1659    2021-05-29       8317        100.0\n",
+       "2         656683     7107    2021-05-09         10          0.0\n",
+       "3         864613     7638    2021-07-05      14483        100.0\n",
+       "4         964868     9506    2021-04-30       6725        100.0\n",
+       "...          ...      ...           ...        ...          ...\n",
+       "5476245   786732     4880    2021-05-12        753          0.0\n",
+       "5476246   648596    12225    2021-08-13         76          0.0\n",
+       "5476247   546862     9673    2021-04-13       2308         49.0\n",
+       "5476249   384202    16197    2021-04-19       6203        100.0\n",
+       "5476250   319709     4436    2021-08-15       3921         45.0\n",
+       "\n",
+       "[5051815 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#разделим данные на обучающую и тестовую выборку - для теста возьмем последние 7 дней\n",
+    "test = df[df['last_watch_dt'] > '2021-08-15']\n",
+    "train = df[df['last_watch_dt'] <= '2021-08-15']\n",
+    "train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "893fd034-c28a-47e8-a8d7-38a27f17991c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "user_id          906071\n",
+       "item_id           15577\n",
+       "last_watch_dt       156\n",
+       "total_dur        126663\n",
+       "watched_pct         101\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0be849ba-66fb-4a28-ab63-81fb740c38e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "user_id          167348\n",
+       "item_id            7106\n",
+       "last_watch_dt         7\n",
+       "total_dur         38328\n",
+       "watched_pct         101\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a9253f90-8245-449c-a119-d68faba82874",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#предположим, что процент времени просмотра линейно связан с вероятностью того, что контент понравился\n",
+    "\n",
+    "train_df = train[['user_id', 'item_id', 'watched_pct']].copy()\n",
+    "test_df = test[['user_id', 'item_id', 'watched_pct']].copy()\n",
+    "\n",
+    "train_df['watched_pct'] = train_df['watched_pct'] / 100\n",
+    "test_df['watched_pct'] = test_df['watched_pct'] / 100\n",
+    "\n",
+    "#создадим разреженные матрицы взаимодействий users-items для обучающей и тестовой выборок\n",
+    "\n",
+    "userid = list(train_df['user_id'].unique())\n",
+    "itemid = list(train_df['item_id'].unique())\n",
+    "data = train_df['watched_pct'].tolist()\n",
+    "row = pd.Categorical(train_df['user_id'], categories=userid, ordered=True).codes\n",
+    "col = pd.Categorical(train_df['item_id'], categories=itemid, ordered=True).codes\n",
+    "train_matrix = csr_matrix((data, (row, col)), shape=(len(userid), len(itemid)))\n",
+    "\n",
+    "userid = list(test_df['user_id'].unique())\n",
+    "itemid = list(test_df['item_id'].unique())\n",
+    "data = test_df['watched_pct'].tolist()\n",
+    "row = pd.Categorical(test_df['user_id'], categories=userid, ordered=True).codes\n",
+    "col = pd.Categorical(test_df['item_id'], categories=itemid, ordered=True).codes\n",
+    "test_matrix = csr_matrix((data, (row, col)), shape=(len(userid), len(itemid)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "289cbc71-933d-400f-88c0-43144cf15977",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<906071x15577 sparse matrix of type '<class 'numpy.float64'>'\n",
+       " \twith 5051815 stored elements in Compressed Sparse Row format>,\n",
+       " <167348x7106 sparse matrix of type '<class 'numpy.float64'>'\n",
+       " \twith 424436 stored elements in Compressed Sparse Row format>)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_matrix, test_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "54cd1509-5a95-4ce1-9048-c0a9f2fe253a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e3828e203024fd585b547eab0c55daa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#обучим рекомендательный алгоритм\n",
+    "\n",
+    "model = AlternatingLeastSquares(factors=64, regularization=0.05, random_state=42)\n",
+    "model.fit(train_matrix)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c5f9f955-2e28-4d0a-86c2-6154a5acc3b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dda9a5f55d14f3aabf97a005196c830",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/167348 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0034842071949627938"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#посчитаем метрику map@10 \n",
+    "mean_average_precision_at_k(model, train_matrix, test_matrix, K=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5be3eace-facb-441e-819a-eb9ae4bef5b2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}