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-------------------------------------------------------------------------------- 1 | # Zero-Shot Recommendation with Language Modeling 2 | Resources accompanying the "Zero-Shot Recommendation as Language Modeling" paper published at ECIR2022, where we show that pretrained large language models can act as a recommender system, and compare few-shot learning results to matrix factorization baselines. 3 | 4 | # Huggingface dataset 5 | We provide a version of our dataset on Huggingface datasets 🤗: 6 | ```python 7 | from datasets import load_dataset 8 | 9 | dataset = load_dataset("sileod/movie_recommendation") 10 | ``` 11 | A version of this dataset was also included in BIG-Bench. 12 | 13 | # Citation 14 | ```bibtex 15 | @InProceedings{sileo-lmrec-2022, 16 | author="Sileo, Damien 17 | and Vossen, Wout 18 | and Raymaekers, Robbe", 19 | editor="Hagen, Matthias 20 | and Verberne, Suzan 21 | and Macdonald, Craig 22 | and Seifert, Christin 23 | and Balog, Krisztian 24 | and N{\o}rv{\aa}g, Kjetil 25 | and Setty, Vinay", 26 | title="Zero-Shot Recommendation as Language Modeling", 27 | booktitle="Advances in Information Retrieval", 28 | year="2022", 29 | publisher="Springer International Publishing", 30 | address="Cham", 31 | pages="223--230", 32 | isbn="978-3-030-99739-7" 33 | } 34 | 35 | 36 | ``` 37 | -------------------------------------------------------------------------------- /lm_rec_ECIR.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "accelerator": "GPU", 6 | "colab": { 7 | "name": "lm-rec ECIR.ipynb", 8 | "provenance": [], 9 | "collapsed_sections": [], 10 | "authorship_tag": "ABX9TyPBQ7A3HFQSdy2Q732ljjNI", 11 | "include_colab_link": true 12 | }, 13 | "kernelspec": { 14 | "display_name": "Python 3", 15 | "name": "python3" 16 | }, 17 | "language_info": { 18 | "name": "python" 19 | } 20 | }, 21 | "cells": [ 22 | { 23 | "cell_type": "markdown", 24 | "metadata": { 25 | "id": "view-in-github", 26 | "colab_type": "text" 27 | }, 28 | "source": [ 29 | "\"Open" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "source": [ 35 | "# Huggingface dataset" 36 | ], 37 | "metadata": { 38 | "id": "Lcldtb7c-2Bu" 39 | } 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "source": [ 44 | "https://huggingface.co/datasets/sileod/movie_recommendation" 45 | ], 46 | "metadata": { 47 | "id": "pqeng7GA_F3M" 48 | } 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": { 53 | "id": "Ih_YzZd6SDB5" 54 | }, 55 | "source": [ 56 | "# Imports" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "metadata": { 62 | "id": "2437VdU6P7_w" 63 | }, 64 | "source": [ 65 | "!pip install GPUtil dropbox xpflow wandb wget -q &> /dev/null" 66 | ], 67 | "execution_count": null, 68 | "outputs": [] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "metadata": { 73 | "id": "20YBuD7jJNVI" 74 | }, 75 | "source": [ 76 | "!pip install git+https://github.com/google/BIG-bench.git -q &> /dev/null" 77 | ], 78 | "execution_count": null, 79 | "outputs": [] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "metadata": { 84 | "id": "HOcZ6jYBLGEy" 85 | }, 86 | "source": [ 87 | "from xpflow import Xp\n", 88 | "from bigbench.api import json_task\n", 89 | "import bigbench.models.huggingface_models as huggingface_models\n", 90 | "import bigbench.api.model as api_model\n", 91 | "from tensorflow.keras import mixed_precision\n", 92 | "import pandas as pd\n", 93 | "import numpy as np\n", 94 | "import random\n", 95 | "import torch\n", 96 | "import functools\n", 97 | "import json\n", 98 | "from tqdm.auto import tqdm\n", 99 | "from datetime import datetime\n", 100 | "import sklearn\n", 101 | "from easydict import EasyDict as edict \n", 102 | "from collections import defaultdict\n", 103 | "from itertools import chain\n", 104 | "import hashlib\n", 105 | "import wget,zipfile, os\n", 106 | "import wandb\n", 107 | "from appdirs import user_data_dir\n", 108 | "import pathlib\n", 109 | "\n", 110 | "\n", 111 | "def precision_recall(y_true,y_pred,k):\n", 112 | " nz = pd.DataFrame(y_true.nonzero()).T\n", 113 | " nz.columns = ['user','item']\n", 114 | " nz = np.array(list(nz.groupby('user')['item'].agg(list)))\n", 115 | "\n", 116 | " precision,recall=[],[]\n", 117 | " for true,pred in zip(nz, (-y_pred).argsort(axis=1)[:,:k]):\n", 118 | " u_recall=np.mean([x in pred for x in true])\n", 119 | " u_precision=np.mean([x in true for x in pred])\n", 120 | " precision+=[u_precision]\n", 121 | " recall+=[u_recall]\n", 122 | " return {f'precision_{k}':np.mean(precision), f'recall_{k}':np.mean(recall)}\n", 123 | "\n", 124 | "def make_metrics(y_true, y_pred):\n", 125 | " metrics=defaultdict(list)\n", 126 | " for k in [1,2,3,4,5]:\n", 127 | " for i in range(len(y_true)):\n", 128 | " yt, yp = y_true[[i],:], y_pred[[i],:]\n", 129 | " metrics[f'ndcg_{k}']+=[sklearn.metrics.ndcg_score(y_true=yt, y_score=yp,k=k)]\n", 130 | " metrics[f'precision_{k}']+=[precision_recall(yt,yp,k)[f'precision_{k}']]\n", 131 | " metrics[f'recall_{k}']+=[precision_recall(yt,yp,k)[f'recall_{k}']]\n", 132 | "\n", 133 | " for m in list(metrics.keys()):\n", 134 | " metrics[f'{m}_std']=np.std(metrics[m])\n", 135 | " metrics[m]=np.mean(metrics[m])\n", 136 | "\n", 137 | " return dict(metrics)\n", 138 | "\n", 139 | "def make_pop(y_pred, y_pops):\n", 140 | " pop_1=[]\n", 141 | " for pops, i in zip(y_pops, y_pred.argmax(axis=1)):\n", 142 | " pop_1+=[pops[i]]\n", 143 | " return {'pop_1':np.mean(pop_1), 'pop_1_std':np.std(pop_1)}" 144 | ], 145 | "execution_count": null, 146 | "outputs": [] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": { 151 | "id": "zTmFZW-gSIak" 152 | }, 153 | "source": [ 154 | "# Build data" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "metadata": { 160 | "id": "N7xksXrCCnNd" 161 | }, 162 | "source": [ 163 | "root = pathlib.Path(user_data_dir(\"gpt-rec\"))\n", 164 | "root.mkdir(exist_ok=True)\n", 165 | "os.chdir(root)\n", 166 | "url = 'https://files.grouplens.org/datasets/movielens/ml-1m.zip'\n", 167 | "\n", 168 | "if not os.path.exists('ml-1m'):\n", 169 | " filename = wget.download(url)\n", 170 | " zipfile.ZipFile(filename).extractall()\n", 171 | "os.chdir('ml-1m')\n", 172 | "\n", 173 | "def process_movielens_name(s):\n", 174 | " s=s[:-7]\n", 175 | " s=s.split(' (')[0]\n", 176 | " for pattern in [', The',', A']:\n", 177 | " if s.endswith(pattern):\n", 178 | " s=pattern.split(', ')[1]+' ' + s.replace(pattern,'')\n", 179 | " return s\n", 180 | "\n", 181 | "items = pd.read_csv('movies.dat',sep='::',names=['movieId','title','genres'], engine='python',encoding=\"latin-1\")\n", 182 | "items['name'] = items.title.map(process_movielens_name)\n", 183 | "item_id_to_name = items.set_index('movieId')['name'].to_dict()\n", 184 | "\n", 185 | "prompts = ('[M]',#0\n", 186 | "'Movies like [M]',#1\n", 187 | "'Movies similar to [M]',#2\n", 188 | "'Movies like: [M]',#3\n", 189 | "'Movies similar to: [M]',#4\n", 190 | "'If you liked [M] you will also like')#5\n", 191 | "\n", 192 | "def make_prompt(l, xp):\n", 193 | " movies = xp.sep.join(random.sample([item_id_to_name[i] for i in l], xp.nb_pos))\n", 194 | " prompt = prompts[xp.prompt_id].replace('[M]', movies) \n", 195 | " return prompt + xp.end_sep\n", 196 | "\n", 197 | "def make_data(xp):\n", 198 | " df = pd.read_csv('ratings.dat',sep=\"::\", names=['userId','movieId','rating','ts'], engine='python')\n", 199 | " df=df[~df.rating.between(2.4,4.1)]\n", 200 | " R = df.pivot('movieId','userId','rating')\n", 201 | "\n", 202 | " pos_neg = df.groupby('userId')['movieId'].agg(list).reset_index().sample(frac=1.0, random_state=xp.users_seed)\n", 203 | " pos_neg[\"pos\"]=pos_neg.apply(lambda x: [i for i in x.movieId if R[x.userId][i]>xp.like_threshold], axis=1)\n", 204 | " pos_neg[\"neg\"]=pos_neg.apply(lambda x: [i for i in x.movieId if R[x.userId][i]