{"id":28306851,"url":"https://github.com/applied-machine-learning-lab/hamur","last_synced_at":"2025-08-16T08:21:26.288Z","repository":{"id":187165737,"uuid":"676420074","full_name":"Applied-Machine-Learning-Lab/HAMUR","owner":"Applied-Machine-Learning-Lab","description":"Code implementation of  \"HAMUR: Hyper Adapter for Multi-Domain Recommendation\" in CIKM‘2023","archived":false,"fork":false,"pushed_at":"2024-01-03T18:21:13.000Z","size":929,"stargazers_count":23,"open_issues_count":0,"forks_count":4,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-20T04:38:15.659Z","etag":null,"topics":["ctr-prediction","domain-adaptation","multi-domain","multi-domain-learning","multi-domain-recommendation","multi-scenario","multi-scenario-recommendation","recommender-system"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Applied-Machine-Learning-Lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-08-09T06:52:53.000Z","updated_at":"2025-04-14T07:52:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"bb0fea51-3623-43b1-bf28-152351c929c1","html_url":"https://github.com/Applied-Machine-Learning-Lab/HAMUR","commit_stats":null,"previous_names":["applied-machine-learning-lab/hamur"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Applied-Machine-Learning-Lab/HAMUR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Applied-Machine-Learning-Lab%2FHAMUR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Applied-Machine-Learning-Lab%2FHAMUR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Applied-Machine-Learning-Lab%2FHAMUR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Applied-Machine-Learning-Lab%2FHAMUR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Applied-Machine-Learning-Lab","download_url":"https://codeload.github.com/Applied-Machine-Learning-Lab/HAMUR/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Applied-Machine-Learning-Lab%2FHAMUR/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270684566,"owners_count":24627829,"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","status":"online","status_checked_at":"2025-08-16T02:00:11.002Z","response_time":91,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["ctr-prediction","domain-adaptation","multi-domain","multi-domain-learning","multi-domain-recommendation","multi-scenario","multi-scenario-recommendation","recommender-system"],"created_at":"2025-05-24T04:12:49.460Z","updated_at":"2025-08-16T08:21:26.276Z","avatar_url":"https://github.com/Applied-Machine-Learning-Lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# HAMUR\n\nOfficial implementation of our paper [HAMUR: Hyper Adapter for Multi-Domain Recommendation](https://arxiv.org/pdf/2309.06217.pdf) in CIKM 2023.\n\nYou could cite our paper if you find this repository interesting or helpful:\n```\n@inproceedings{li2023hamur,\n  title={HAMUR: Hyper Adapter for Multi-Domain Recommendation},\n  author={Li, Xiaopeng and Yan, Fan and Zhao, Xiangyu and Wang, Yichao and Chen, Bo and Guo, Huifeng and Tang, Ruiming},\n  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},\n  pages={1268--1277},\n  year={2023}\n}\n```\n\n## Introduction\nSource code of HAMUR: Hyper Adapter for Multi-Domain Recommendation, in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management(CIKM 23').\n!['Img_HAMUR'](framework.jpg)\n\n## Environment Setting\n* torch \u003e=1.7.0\n* numpy \u003e=1.23.5\n* pandas \u003e=1.5.3\n* scikit-learn \u003e=0.23.2\n\n## Dataset Download\nIn this paper, we use two datasets, **Aliccp** and **movieLens**. Dataset samples are shown in example/data.\n\nFull dataset download:\n* Aliccp: Download address https://tianchi.aliyun.com/dataset/408.\n* Movielens: The raw data file can be found in [Torch-Rechub-ml-1m](https://github.com/morningsky/Torch-RecHub/tree/main/examples/matching/data/ml-1m), and you could directly download the processed file from https://cowtransfer.com/s/5a3ab69ebd314e.\n\n## Models\nIn this repo, we offer the following models. Their structures are shown in the following figure.\n!['Img_DifferentBackbone'](DifferentBackbone.jpg)\n* Pure MLP as multi-domain backbone models.\n* MLP + HAMUR\n* Pure Wide \u0026 Deep as multi-domain backbone models.\n* Wide \u0026 Deep + HAMUR\n* Pure DCN as multi-domain backbone models. \n* DCN + HAMUR\n\n## Usage\n\n### Step 1: Clone the repository\n```Shell\ngit clone https://github.com/Applied-Machine-Learning-Lab/HAMUR.git\n```\n\n### Step 2: Run the model\n```Shell\ncd examples\n# For Aliccp\npython run_ali_ccp_ctr_ranking_multi_domain.py --model_name mlp_adp --epoch 200 --device cpu --seed 2022 \n# For MovieLens\npython run_movielens_rank_multi_domain.py --model_name mlp_adp --epoch 200 --device cpu --seed 2022 \n\n```\n\n## Credits\nOur code is developed based on [Torch-RecHub](https://github.com/datawhalechina/torch-rechub). Thanks to their contribution.\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapplied-machine-learning-lab%2Fhamur","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapplied-machine-learning-lab%2Fhamur","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapplied-machine-learning-lab%2Fhamur/lists"}