From 336ab68e1fda9b3ac1aef1e6f53a6661ac307ede Mon Sep 17 00:00:00 2001 From: arsalanyavari Date: Sun, 11 Aug 2024 07:34:35 +0330 Subject: [PATCH] add recommender systems --- recommender_system/collaborative/.gitignore | 3 + .../collaborative/collaborative.ipynb | 2688 ++++++++++ .../collaborative/dataset/archive.zip | Bin 0 -> 10407949 bytes recommender_system/content_based/.gitignore | 3 + .../content_based/content_based.ipynb | 4663 +++++++++++++++++ .../content_based/dataset/archive.zip | Bin 0 -> 3772978 bytes 6 files changed, 7357 insertions(+) create mode 100644 recommender_system/collaborative/.gitignore create mode 100644 recommender_system/collaborative/collaborative.ipynb create mode 100644 recommender_system/collaborative/dataset/archive.zip create mode 100644 recommender_system/content_based/.gitignore create mode 100644 recommender_system/content_based/content_based.ipynb create mode 100644 recommender_system/content_based/dataset/archive.zip diff --git a/recommender_system/collaborative/.gitignore b/recommender_system/collaborative/.gitignore new file mode 100644 index 0000000..985e24b --- /dev/null +++ b/recommender_system/collaborative/.gitignore @@ -0,0 +1,3 @@ +*.csv +.ipynb_checkpoints +*.png diff --git a/recommender_system/collaborative/collaborative.ipynb b/recommender_system/collaborative/collaborative.ipynb new file mode 100644 index 0000000..f8f2e1c --- /dev/null +++ b/recommender_system/collaborative/collaborative.ipynb @@ -0,0 +1,2688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "29b5381a-72b4-4f81-9208-2075f7acad85", + "metadata": {}, + "source": [ + "# Collaborative Recommender System" + ] + }, + { + "cell_type": "markdown", + "id": "cac03d32", + "metadata": {}, + "source": [ + "Configure the project. Indeed you create a dataset in csv format." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6f480cda-8380-4355-998a-5c59d6203b05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archive: ./dataset/archive.zip\n", + " inflating: anime.csv \n", + " inflating: rating.csv \n" + ] + } + ], + "source": [ + "! rm -rf *.csv\n", + "! unzip ./dataset/archive.zip\n" + ] + }, + { + "cell_type": "markdown", + "id": "52ec2f48", + "metadata": {}, + "source": [ + "Import needed libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd17f780", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from IPython.display import Image\n", + "from scipy.stats import pearsonr\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "d836411b", + "metadata": {}, + "source": [ + "### User-Based Collaborative Filtering\n", + "- Approach: Finds users similar to the target user based on historical interactions.\n", + "- Process:\n", + " 1. Identify users with similar preferences.\n", + " 2. Recommend items liked by these similar users.\n", + "- Pros:\n", + " - Simple to understand and implement.\n", + " - Often effective with sufficient user data.\n", + "- Cons:\n", + " - Performance degrades with large datasets.\n", + " - Struggles with new users (cold start problem).\n", + "\n", + "### Item-Based Collaborative Filtering\n", + "- Approach: Finds items similar to the ones the target user has interacted with.\n", + "- Process:\n", + " 1. Identify items similar to what the user likes.\n", + " 2. Recommend these similar items.\n", + "- Pros:\n", + " - More scalable with large datasets.\n", + " - Can leverage item characteristics and interactions.\n", + "- Cons:\n", + " - Requires significant item interaction data.\n", + " - Might not capture nuanced user preferences.\n", + "\n", + "Both approaches aim to provide personalized recommendations but differ in their method and scalability." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d7b51235", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://www.scaler.com/topics/images/collaborative.webp\"\n", + "Image(url=url)" + ] + }, + { + "cell_type": "markdown", + "id": "162cb46a", + "metadata": {}, + "source": [ + "### I decided to implement User Based Approach to avoid memory issues." + ] + }, + { + "cell_type": "markdown", + "id": "57b33a77", + "metadata": {}, + "source": [ + "Read data from csv files using pandas and store in data frame structure. Also shuffle data to have uniform distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a102a751", + "metadata": {}, + "outputs": [], + "source": [ + "anime_df = pd.read_csv(\"anime.csv\")\n", + "anime_df = anime_df.sample(frac=1.0, random_state=42).reset_index(drop=True)\n", + "\n", + "rating_df = pd.read_csv(\"rating.csv\")\n", + "rating_df = rating_df.sample(frac=1.0, random_state=42).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e415ec3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1173TacticsComedy, Drama, Fantasy, Mystery, Shounen, Supe...TV257.3427358
23616Kamen no Maid GuyAction, Comedy, Ecchi, Super PowerTV127.1427761
318799Take Your WayAction, Music, Seinen, SupernaturalMusic16.661387
418831RinkakuDementia, Horror, MusicMusic15.60606
........................
122894638MilkywayHentai, RomanceOVA25.82695
122905272Tondemo Nezumi DaikatsuyakuAdventureMovie16.53252
122911262Macross II: Lovers AgainAdventure, Mecha, Military, Sci-Fi, Shounen, S...OVA66.476760
1229222819Aikatsu! MovieMusic, School, Shoujo, Slice of LifeMovie17.792813
122932364Virus: Virus Buster SergeAction, Adventure, Mecha, Police, Sci-FiTV125.592250
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TitleRatinganime_id
0Boku dake ga Inai Machi10.031043.0
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12Kimi no Suizou wo Tabetai8.5NaN
13Mononoke Hime7.5164.0
14Sen to Chihiro no Kamikakushi7.5199.0
15Koe no Katachi8.528851.0
16Ao Haru Ride5.521995.0
17Toki wo Kakeru Shoujo7.02236.0
18Another7.511111.0
19Kimetsu no Yaiba7.0NaN
20Shigatsu wa Kimi no Uso8.023273.0
21Byousoku 5 Centimeter6.01689.0
22Kokoro ga Sakebitagatterunda.7.528725.0
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TitleRatinganime_id
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17Byousoku 5 Centimeter6.01689.0
18Kokoro ga Sakebitagatterunda.7.528725.0
19Schick x Evangelion5.031115.0
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" + ], + "text/plain": [ + " Title Rating anime_id\n", + "0 Boku dake ga Inai Machi 10.0 31043.0\n", + "1 Violet Evergarden 9.5 33352.0\n", + "2 Berserk 8.0 33.0\n", + "3 Shingeki no Kyojin 7.0 16498.0\n", + "4 Tokyo Ghoul 6.5 22319.0\n", + "5 Orange 6.0 32729.0\n", + "6 Death Parade 8.0 28223.0\n", + "7 Death Note 7.5 1535.0\n", + "8 Bungou Stray Dogs 7.5 31478.0\n", + "9 Kimi no Na wa. 8.0 32281.0\n", + "10 Mononoke Hime 7.5 164.0\n", + "11 Sen to Chihiro no Kamikakushi 7.5 199.0\n", + "12 Koe no Katachi 8.5 28851.0\n", + "13 Ao Haru Ride 5.5 21995.0\n", + "14 Toki wo Kakeru Shoujo 7.0 2236.0\n", + "15 Another 7.5 11111.0\n", + "16 Shigatsu wa Kimi no Uso 8.0 23273.0\n", + "17 Byousoku 5 Centimeter 6.0 1689.0\n", + "18 Kokoro ga Sakebitagatterunda. 7.5 28725.0\n", + "19 Schick x Evangelion 5.0 31115.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inputAnime = inputAnime.dropna(subset=['anime_id'])\n", + "inputAnime = inputAnime.reset_index(drop=True)\n", + "\n", + "inputAnime" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6c203367", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idanime_idrating
2206853232739
40210089219958
60135393310
760289553228110
762239822319-1
............
2925331121153510
29254581256153510
292555614421648
29256699625219958
292568536521649
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17078 rows × 3 columns

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" + ], + "text/plain": [ + " user_id anime_id rating\n", + "220 6853 23273 9\n", + "402 10089 21995 8\n", + "601 3539 33 10\n", + "760 28955 32281 10\n", + "762 2398 22319 -1\n", + "... ... ... ...\n", + "2925331 121 1535 10\n", + "2925458 1256 1535 10\n", + "2925556 1442 164 8\n", + "2925669 9625 21995 8\n", + "2925685 3652 164 9\n", + "\n", + "[17078 rows x 3 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "userSubset = rating_df[rating_df['anime_id'].isin(inputAnime['anime_id'].tolist())]\n", + "userSubset" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dd719111", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idcountsanime_ids
55268715[21995, 2236, 1689, 22319, 1535, 164, 28725, 2...
920114514[16498, 31043, 21995, 28725, 2236, 31478, 2327...
2479333814[28851, 32281, 31478, 22319, 21995, 2236, 1689...
62578414[28223, 22319, 1535, 32729, 21995, 31043, 1649...
60676013[22319, 28223, 31043, 31478, 199, 32729, 1535,...
............
7082680911[28725]
7083681771[28725]
7084683201[28725]
7085684051[28725]
7086685591[28725]
\n", + "

7133 rows × 3 columns

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" + ], + "text/plain": [ + " user_id counts anime_ids\n", + "552 687 15 [21995, 2236, 1689, 22319, 1535, 164, 28725, 2...\n", + "920 1145 14 [16498, 31043, 21995, 28725, 2236, 31478, 2327...\n", + "2479 3338 14 [28851, 32281, 31478, 22319, 21995, 2236, 1689...\n", + "625 784 14 [28223, 22319, 1535, 32729, 21995, 31043, 1649...\n", + "606 760 13 [22319, 28223, 31043, 31478, 199, 32729, 1535,...\n", + "... ... ... ...\n", + "7082 68091 1 [28725]\n", + "7083 68177 1 [28725]\n", + "7084 68320 1 [28725]\n", + "7085 68405 1 [28725]\n", + "7086 68559 1 [28725]\n", + "\n", + "[7133 rows x 3 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "userSubsetGroup = userSubset.groupby('user_id').agg(\n", + " counts=('anime_id', 'count'), # Count of anime\n", + " anime_ids=('anime_id', lambda x: list(x)) # List of anime IDs\n", + ").reset_index()\n", + "\n", + "userSubsetGroup = userSubsetGroup.sort_values(by='counts', ascending=False)\n", + "userSubsetGroup" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "637a3307", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idcountsanime_ids
55268715[21995, 2236, 1689, 22319, 1535, 164, 28725, 2...
920114514[16498, 31043, 21995, 28725, 2236, 31478, 2327...
2479333814[28851, 32281, 31478, 22319, 21995, 2236, 1689...
62578414[28223, 22319, 1535, 32729, 21995, 31043, 1649...
60676013[22319, 28223, 31043, 31478, 199, 32729, 1535,...
............
960119710[1689, 16498, 21995, 28725, 28223, 23273, 3147...
1519187010[21995, 164, 31478, 31043, 28223, 22319, 11111...
1530188910[21995, 23273, 11111, 199, 33, 31043, 164, 168...
1752224310[21995, 32729, 1689, 28223, 22319, 11111, 3228...
1495183710[28223, 22319, 31043, 164, 2236, 199, 23273, 2...
\n", + "

97 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " user_id counts anime_ids\n", + "552 687 15 [21995, 2236, 1689, 22319, 1535, 164, 28725, 2...\n", + "920 1145 14 [16498, 31043, 21995, 28725, 2236, 31478, 2327...\n", + "2479 3338 14 [28851, 32281, 31478, 22319, 21995, 2236, 1689...\n", + "625 784 14 [28223, 22319, 1535, 32729, 21995, 31043, 1649...\n", + "606 760 13 [22319, 28223, 31043, 31478, 199, 32729, 1535,...\n", + "... ... ... ...\n", + "960 1197 10 [1689, 16498, 21995, 28725, 28223, 23273, 3147...\n", + "1519 1870 10 [21995, 164, 31478, 31043, 28223, 22319, 11111...\n", + "1530 1889 10 [21995, 23273, 11111, 199, 33, 31043, 164, 168...\n", + "1752 2243 10 [21995, 32729, 1689, 28223, 22319, 11111, 3228...\n", + "1495 1837 10 [28223, 22319, 31043, 164, 2236, 199, 23273, 2...\n", + "\n", + "[97 rows x 3 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "userSubsetGroup = userSubsetGroup[userSubsetGroup['counts'] >= 10]\n", + "userSubsetGroup" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4d44029f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User ID: 687, Pearson Correlation: -0.23953974895397492\n", + "User ID: 1145, Pearson Correlation: -0.018181818181818202\n", + "User ID: 3338, Pearson Correlation: 0.18984771573604062\n", + "User ID: 784, Pearson Correlation: 0.14410480349344976\n", + "User ID: 760, Pearson Correlation: -0.33180428134556567\n", + "User ID: 392, Pearson Correlation: 0.12532981530343007\n", + "User ID: 446, Pearson Correlation: -0.6027397260273972\n", + "User ID: 17, Pearson Correlation: -0.4708029197080291\n", + "User ID: 342, Pearson Correlation: 0.10817941952506599\n", + "User ID: 786, Pearson Correlation: -0.0633245382585752\n", + "User ID: 1497, Pearson Correlation: 0.294811320754717\n", + "User ID: 963, Pearson Correlation: 0.08401084010840111\n", + "User ID: 2378, Pearson Correlation: 0.3827751196172249\n", + "User ID: 813, Pearson Correlation: -0.1868131868131868\n", + "User ID: 958, Pearson Correlation: -0.3411764705882353\n", + "User ID: 938, Pearson Correlation: 0.6970873786407766\n", + "User ID: 198, Pearson Correlation: 0.1\n", + "User ID: 562, Pearson Correlation: 0.1047957371225577\n", + "User ID: 1013, Pearson Correlation: 0.6026490066225164\n", + "User ID: 1597, Pearson Correlation: -0.2529411764705883\n", + "User ID: 3299, Pearson Correlation: -0.10982658959537578\n", + "User ID: 1222, Pearson Correlation: -0.03311258278145697\n", + "User ID: 123, Pearson Correlation: 0.15761589403973508\n", + "User ID: 77, Pearson Correlation: 0.10773899848254934\n", + "User ID: 1378, Pearson Correlation: 0.2\n", + "User ID: 1344, Pearson Correlation: 0.039999999999999994\n", + "User ID: 1327, Pearson Correlation: -0.3511111111111111\n", + "User ID: 1813, Pearson Correlation: -0.041176470588235294\n", + "User ID: 271, Pearson Correlation: 0.536082474226804\n", + "User ID: 1290, Pearson Correlation: 0.05962521294718907\n", + "User ID: 1019, Pearson Correlation: 0.31746031746031733\n", + "User ID: 478, Pearson Correlation: 0.3470588235294118\n", + "User ID: 2864, Pearson Correlation: -0.19111111111111112\n", + "User ID: 1456, Pearson Correlation: 0.27472527472527475\n", + "User ID: 1274, Pearson Correlation: -0.1502145922746781\n", + "User ID: 651, Pearson Correlation: 0.10724637681159421\n", + "User ID: 611, Pearson Correlation: -0.33333333333333337\n", + "User ID: 2318, Pearson Correlation: 0.09130434782608696\n", + "User ID: 1176, Pearson Correlation: 0.6577777777777776\n", + "User ID: 2200, Pearson Correlation: -0.6019417475728154\n", + "User ID: 585, Pearson Correlation: 1.7094345118926147e-18\n", + "User ID: 2016, Pearson Correlation: 0.35069444444444436\n", + "User ID: 1394, Pearson Correlation: -0.021428571428571443\n", + "User ID: 1116, Pearson Correlation: -0.03125000000000002\n", + "User ID: 1620, Pearson Correlation: -0.12780898876404492\n", + "User ID: 1435, Pearson Correlation: 0.3058252427184466\n", + "User ID: 1442, Pearson Correlation: -0.0298804780876494\n", + "User ID: 395, Pearson Correlation: 0.06382978723404255\n", + "User ID: 3284, Pearson Correlation: 0.28864353312302843\n", + "User ID: 3360, Pearson Correlation: -0.04532163742690057\n", + "User ID: 2951, Pearson Correlation: -0.8857142857142858\n", + "User ID: 1418, Pearson Correlation: -0.35714285714285704\n", + "User ID: 2555, Pearson Correlation: 0.017182130584192438\n", + "User ID: 2867, Pearson Correlation: 0.032407407407407406\n", + "User ID: 79, Pearson Correlation: -0.02325581395348838\n", + "User ID: 139, Pearson Correlation: -0.028037383177570086\n", + "User ID: 1815, Pearson Correlation: -0.20833333333333337\n", + "User ID: 1814, Pearson Correlation: -0.3707165109034268\n", + "User ID: 1918, Pearson Correlation: 0.05241935483870963\n", + "User ID: 2256, Pearson Correlation: -0.4411247803163445\n", + "User ID: 228, Pearson Correlation: -0.2588652482269503\n", + "User ID: 565, Pearson Correlation: 0.024822695035461\n", + "User ID: 744, Pearson Correlation: 0.45895522388059706\n", + "User ID: 4102, Pearson Correlation: -0.6903409090909091\n", + "User ID: 1501, Pearson Correlation: -0.16465863453815263\n", + "User ID: 1504, Pearson Correlation: -0.5151515151515152\n", + "User ID: 1343, Pearson Correlation: 0.019607843137254957\n", + "User ID: 4565, Pearson Correlation: 0.13043478260869568\n", + "User ID: 861, Pearson Correlation: -0.44\n", + "User ID: 2202, Pearson Correlation: -0.023454157782515993\n", + "User ID: 553, Pearson Correlation: 0.23809523809523808\n", + "User ID: 4512, Pearson Correlation: 0.13043478260869568\n", + "User ID: 1237, Pearson Correlation: -0.5329768270944742\n", + "User ID: 1252, Pearson Correlation: -0.044176706827309245\n", + "User ID: 1284, Pearson Correlation: -0.02083333333333333\n", + "User ID: 926, Pearson Correlation: -0.11475409836065573\n", + "User ID: 995, Pearson Correlation: -0.40909090909090906\n", + "User ID: 1419, Pearson Correlation: 0.8333333333333333\n", + "User ID: 1579, Pearson Correlation: -0.06339468302658491\n", + "User ID: 1578, Pearson Correlation: -0.045871559633027505\n", + "User ID: 244, Pearson Correlation: -0.20567375886524816\n", + "User ID: 1711, Pearson Correlation: -0.17021276595744678\n", + "User ID: 1400, Pearson Correlation: 0.483065953654189\n", + "User ID: 894, Pearson Correlation: -0.018518518518518556\n", + "User ID: 2025, Pearson Correlation: 0.3023255813953489\n", + "User ID: 598, Pearson Correlation: -0.22340425531914884\n", + "User ID: 1023, Pearson Correlation: 0.21985815602836875\n", + "User ID: 2273, Pearson Correlation: 0.09274193548387095\n", + "User ID: 1605, Pearson Correlation: -0.4262820512820513\n", + "User ID: 1963, Pearson Correlation: 0.3055555555555556\n", + "User ID: 1167, Pearson Correlation: -0.2121212121212121\n", + "User ID: 444, Pearson Correlation: 0.45512820512820523\n", + "User ID: 1197, Pearson Correlation: -0.5261044176706828\n", + "User ID: 1870, Pearson Correlation: -0.3750000000000001\n", + "User ID: 1889, Pearson Correlation: -0.2126537785588753\n", + "User ID: 2243, Pearson Correlation: 0.0657439446366782\n", + "User ID: 1837, Pearson Correlation: -0.22699386503067487\n" + ] + } + ], + "source": [ + "animeRating_dict = inputAnime.set_index('anime_id')['Rating'].to_dict()\n", + "\n", + "pearsonCorrelation_dict = {}\n", + "\n", + "for index, row in userSubsetGroup.iterrows():\n", + " user_id = row['user_id']\n", + " user_anime_ids = row['anime_ids']\n", + " \n", + " # Get corresponding ratings for the user's anime ids\n", + " user_ratings = [animeRating_dict[anime_id] for anime_id in user_anime_ids if anime_id in animeRating_dict]\n", + " \n", + " # Calculate Pearson correlation with inputAnime ratings (use it as a baseline)\n", + " if user_ratings:\n", + " input_ratings = [animeRating_dict[anime_id] for anime_id in inputAnime['anime_id'] if anime_id in user_anime_ids]\n", + " \n", + " # Ensure both lists have the same length\n", + " if len(user_ratings) == len(input_ratings) and len(user_ratings) > 0:\n", + " correlation, _ = pearsonr(user_ratings, input_ratings)\n", + " pearsonCorrelation_dict[user_id] = correlation\n", + " else:\n", + " pearsonCorrelation_dict[user_id] = 0\n", + " else:\n", + " pearsonCorrelation_dict[user_id] = 0\n", + "\n", + "# Display the results\n", + "for user_id, correlation in pearsonCorrelation_dict.items():\n", + " print(f\"User ID: {user_id}, Pearson Correlation: {correlation}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5c852fca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Similarity Indexuser_id
0-0.239540687
1-0.0181821145
20.1898483338
30.144105784
4-0.331804760
.........
92-0.5261041197
93-0.3750001870
94-0.2126541889
950.0657442243
96-0.2269941837
\n", + "

97 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " Similarity Index user_id\n", + "0 -0.239540 687\n", + "1 -0.018182 1145\n", + "2 0.189848 3338\n", + "3 0.144105 784\n", + "4 -0.331804 760\n", + ".. ... ...\n", + "92 -0.526104 1197\n", + "93 -0.375000 1870\n", + "94 -0.212654 1889\n", + "95 0.065744 2243\n", + "96 -0.226994 1837\n", + "\n", + "[97 rows x 2 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pearsonDF = pd.DataFrame.from_dict(pearsonCorrelation_dict, orient='index')\n", + "pearsonDF.columns = ['Similarity Index']\n", + "pearsonDF['user_id'] = pearsonDF.index\n", + "pearsonDF.index = range(len(pearsonDF))\n", + "pearsonDF" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5e86597a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Similarity Indexuser_id
770.8333331419
150.697087938
380.6577781176
180.6026491013
280.536082271
820.4830661400
620.458955744
910.455128444
120.3827752378
410.3506942016
310.347059478
300.3174601019
450.3058251435
890.3055561963
840.3023262025
100.2948111497
480.2886443284
330.2747251456
700.238095553
860.2198581023
240.2000001378
20.1898483338
220.157616123
30.144105784
670.1304354565
710.1304354512
50.125330392
80.108179342
230.10773977
350.107246651
170.104796562
160.100000198
870.0927422273
370.0913042318
110.084011963
950.0657442243
470.063830395
290.0596251290
580.0524191918
250.0400001344
\n", + "
" + ], + "text/plain": [ + " Similarity Index user_id\n", + "77 0.833333 1419\n", + "15 0.697087 938\n", + "38 0.657778 1176\n", + "18 0.602649 1013\n", + "28 0.536082 271\n", + "82 0.483066 1400\n", + "62 0.458955 744\n", + "91 0.455128 444\n", + "12 0.382775 2378\n", + "41 0.350694 2016\n", + "31 0.347059 478\n", + "30 0.317460 1019\n", + "45 0.305825 1435\n", + "89 0.305556 1963\n", + "84 0.302326 2025\n", + "10 0.294811 1497\n", + "48 0.288644 3284\n", + "33 0.274725 1456\n", + "70 0.238095 553\n", + "86 0.219858 1023\n", + "24 0.200000 1378\n", + "2 0.189848 3338\n", + "22 0.157616 123\n", + "3 0.144105 784\n", + "67 0.130435 4565\n", + "71 0.130435 4512\n", + "5 0.125330 392\n", + "8 0.108179 342\n", + "23 0.107739 77\n", + "35 0.107246 651\n", + "17 0.104796 562\n", + "16 0.100000 198\n", + "87 0.092742 2273\n", + "37 0.091304 2318\n", + "11 0.084011 963\n", + "95 0.065744 2243\n", + "47 0.063830 395\n", + "29 0.059625 1290\n", + "58 0.052419 1918\n", + "25 0.040000 1344" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topUsers = pearsonDF.sort_values(by='Similarity Index', ascending=False)[0:40]\n", + "topUsers" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "82c7d302", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Similarity Indexuser_idanime_idrating
00.83333314192331910
10.83333314193104310
20.8333331419290410
30.8333331419133919
40.83333314191418910
...............
181810.0400001344239-1
181820.0400001344304139
181830.04000013449863-1
181840.040000134490417
181850.04000013443654-1
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18186 rows × 4 columns

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" + ], + "text/plain": [ + " Similarity Index user_id anime_id rating\n", + "0 0.833333 1419 23319 10\n", + "1 0.833333 1419 31043 10\n", + "2 0.833333 1419 2904 10\n", + "3 0.833333 1419 13391 9\n", + "4 0.833333 1419 14189 10\n", + "... ... ... ... ...\n", + "18181 0.040000 1344 239 -1\n", + "18182 0.040000 1344 30413 9\n", + "18183 0.040000 1344 9863 -1\n", + "18184 0.040000 1344 9041 7\n", + "18185 0.040000 1344 3654 -1\n", + "\n", + "[18186 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topUsersRating=topUsers.merge(rating_df, left_on='user_id', right_on='user_id', how='inner')\n", + "topUsersRating" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8d7f5f46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "topUsersRating['weighted Rating'] = topUsersRating['Similarity Index']*topUsersRating['rating']\n", + "topUsersRating.head" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "461da216", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sum of similarity Indexsum of weighted Rating
anime_id
12.94441919.664045
50.8458585.965146
61.2687086.559716
71.0037688.823478
151.0125946.341339
.........
340850.0927420.649194
341030.4681512.845150
341360.0400000.320000
341730.274725-0.274725
342403.38889729.199424
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3503 rows × 2 columns

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" + ], + "text/plain": [ + " sum of similarity Index sum of weighted Rating\n", + "anime_id \n", + "1 2.944419 19.664045\n", + "5 0.845858 5.965146\n", + "6 1.268708 6.559716\n", + "7 1.003768 8.823478\n", + "15 1.012594 6.341339\n", + "... ... ...\n", + "34085 0.092742 0.649194\n", + "34103 0.468151 2.845150\n", + "34136 0.040000 0.320000\n", + "34173 0.274725 -0.274725\n", + "34240 3.388897 29.199424\n", + "\n", + "[3503 rows x 2 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tempTopUsersRating = topUsersRating.groupby('anime_id').sum()[['Similarity Index','weighted Rating']]\n", + "tempTopUsersRating.columns = ['sum of similarity Index','sum of weighted Rating']\n", + "tempTopUsersRating" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b87e3678", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weighted average recommendation scoreanime_id
anime_id
16.6784131
57.0521865
65.1703896
78.7903557
156.26246815
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" + ], + "text/plain": [ + " weighted average recommendation score anime_id\n", + "anime_id \n", + "1 6.678413 1\n", + "5 7.052186 5\n", + "6 5.170389 6\n", + "7 8.790355 7\n", + "15 6.262468 15" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recommendation_df = pd.DataFrame()\n", + "recommendation_df['weighted average recommendation score'] = tempTopUsersRating['sum of weighted Rating']/tempTopUsersRating['sum of similarity Index']\n", + "recommendation_df['anime_id'] = tempTopUsersRating.index\n", + "recommendation_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "60a0e629", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weighted average recommendation scoreanime_id
anime_id
324710.03247
1015310.010153
2847910.028479
2868310.028683
44110.0441
44610.0446
282510.02825
44910.0449
1614310.016143
156610.01566
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" + ], + "text/plain": [ + " weighted average recommendation score anime_id\n", + "anime_id \n", + "3247 10.0 3247\n", + "10153 10.0 10153\n", + "28479 10.0 28479\n", + "28683 10.0 28683\n", + "441 10.0 441\n", + "446 10.0 446\n", + "2825 10.0 2825\n", + "449 10.0 449\n", + "16143 10.0 16143\n", + "1566 10.0 1566" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recommendation_df = recommendation_df.sort_values(by='weighted average recommendation score', ascending=False)\n", + "recommendation_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1dc56a93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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anime_idnamegenretypeepisodesratingmembers
2839449InuYasha: Guren no HouraijimaAdventure, Comedy, Demons, Drama, Historical, ...Movie17.6250008
314116143One Piece: Kinkyuu Kikaku One Piece Kanzen Kou...Adventure, Comedy, Fantasy, ShounenSpecial17.365914
679810153Mahou Shoujo Lyrical Nanoha: The Movie 2nd A&#...Action, Comedy, Drama, Magic, Super PowerMovie18.3413315
689528683One Piece: Episode of Alabasta - PrologueAction, Adventure, Fantasy, ShounenOVA17.414225
7325441Shoujo Kakumei Utena: Adolescence MokushirokuDementia, Drama, Fantasy, Romance, ShoujoMovie17.5922219
7968446Weiß Kreuz GlühenAction, Drama, ShounenTV136.727043
85283247Love Hina Final SelectionComedy, Ecchi, Harem, RomanceOVA17.3221824
86361566Ghost in the Shell: Stand Alone Complex - Soli...Mecha, Military, Mystery, Police, Sci-Fi, SeinenSpecial18.2255247
1157428479Detective Conan Movie 19: The Hellfire SunflowersAction, Mystery, Police, ShounenMovie17.778600
122742825Arabian Nights: Sindbad no Bouken (TV)Adventure, Fantasy, Magic, RomanceTV527.262631
\n", + "
" + ], + "text/plain": [ + " anime_id name \\\n", + "2839 449 InuYasha: Guren no Houraijima \n", + "3141 16143 One Piece: Kinkyuu Kikaku One Piece Kanzen Kou... \n", + "6798 10153 Mahou Shoujo Lyrical Nanoha: The Movie 2nd A&#... \n", + "6895 28683 One Piece: Episode of Alabasta - Prologue \n", + "7325 441 Shoujo Kakumei Utena: Adolescence Mokushiroku \n", + "7968 446 Weiß Kreuz Glühen \n", + "8528 3247 Love Hina Final Selection \n", + "8636 1566 Ghost in the Shell: Stand Alone Complex - Soli... \n", + "11574 28479 Detective Conan Movie 19: The Hellfire Sunflowers \n", + "12274 2825 Arabian Nights: Sindbad no Bouken (TV) \n", + "\n", + " genre type episodes \\\n", + "2839 Adventure, Comedy, Demons, Drama, Historical, ... Movie 1 \n", + "3141 Adventure, Comedy, Fantasy, Shounen Special 1 \n", + "6798 Action, Comedy, Drama, Magic, Super Power Movie 1 \n", + "6895 Action, Adventure, Fantasy, Shounen OVA 1 \n", + "7325 Dementia, Drama, Fantasy, Romance, Shoujo Movie 1 \n", + "7968 Action, Drama, Shounen TV 13 \n", + "8528 Comedy, Ecchi, Harem, Romance OVA 1 \n", + "8636 Mecha, Military, Mystery, Police, Sci-Fi, Seinen Special 1 \n", + "11574 Action, Mystery, Police, Shounen Movie 1 \n", + "12274 Adventure, Fantasy, Magic, Romance TV 52 \n", + "\n", + " rating members \n", + "2839 7.62 50008 \n", + "3141 7.36 5914 \n", + "6798 8.34 13315 \n", + "6895 7.41 4225 \n", + "7325 7.59 22219 \n", + "7968 6.72 7043 \n", + "8528 7.32 21824 \n", + "8636 8.22 55247 \n", + "11574 7.77 8600 \n", + "12274 7.26 2631 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anime_df.loc[anime_df['anime_id'].isin(recommendation_df.head(10)['anime_id'].tolist())]\n" + ] 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