{"id":13458964,"url":"https://github.com/justarter/E2URec","last_synced_at":"2025-03-24T16:31:14.183Z","repository":{"id":226168836,"uuid":"767934771","full_name":"justarter/E2URec","owner":"justarter","description":"Official Code for paper \"Towards Efficient and Effective Unlearning of Large Language Models for Recommendation\" (Frontiers of Computer Science 2024)","archived":false,"fork":false,"pushed_at":"2024-07-19T08:43:43.000Z","size":31,"stargazers_count":34,"open_issues_count":2,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-10-29T04:34:31.596Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/justarter.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-06T06:57:07.000Z","updated_at":"2024-09-05T09:07:59.000Z","dependencies_parsed_at":"2024-07-11T08:47:33.653Z","dependency_job_id":"fc7981e8-9b0e-4f5f-9f40-97f545c9d862","html_url":"https://github.com/justarter/E2URec","commit_stats":null,"previous_names":["justarter/e2urec"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/justarter%2FE2URec","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/justarter%2FE2URec/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/justarter%2FE2URec/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/justarter%2FE2URec/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/justarter","download_url":"https://codeload.github.com/justarter/E2URec/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245308537,"owners_count":20594263,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-31T09:01:00.318Z","updated_at":"2025-03-24T16:31:13.879Z","avatar_url":"https://github.com/justarter.png","language":"Python","funding_links":[],"categories":["Papers"],"sub_categories":["2024"],"readme":"# Towards Efficient and Effective Unlearning of Large Language Models for Recommendation\n## Introduction\nThis is the pytorch implementation of ***E2URec*** proposed in the paper [Towards Efficient and Effective Unlearning of Large Language Models for Recommendation](http://arxiv.org/abs/2403.03536). (Frontiers of Computer Science 2024)\n\n\n## Requirements\n~~~python\npip install -r requirements.txt\n~~~\n\n## Data preprocess\nScripts for data preprocessing are included in data_preprocess.\n\nFirst, use ml-1m.ipynb to preprocess MovieLens-1M.\n\nThen, convert data into text\n~~~python\npython data2json.py --K 10 --temp_type simple --set train --dataset ml-1m\npython data2json.py --K 10 --temp_type simple --set valid --dataset ml-1m\npython data2json.py --K 10 --temp_type simple --set test --dataset ml-1m\n~~~\nFinally, use split_ml-1m.ipynb to split train/valid/test, retained/forgotten data.\n\n## How to run E2URec\nOur method `E2URec` can be trained by\n~~~python\nsh train_e2urec.sh\n~~~\n\n\n\n## How to run baselines\nWe also provide shell scripts for baselines.\n\nTo run the `Retrain` baseline:\n~~~python\nsh train_normal.sh\n~~~\nTo run the `SISA` baseline:\n~~~python\nsh train_sisa.sh\n~~~\nTo run the `NegGrad` baseline:\n~~~python\nsh train_ga.sh\n~~~\nTo run the `Bad-T` baseline:\n~~~python\nsh train_rl.sh\n~~~\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjustarter%2FE2URec","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjustarter%2FE2URec","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjustarter%2FE2URec/lists"}