{"id":22364737,"url":"https://github.com/ahxt/neuacf","last_synced_at":"2025-09-03T02:47:19.410Z","repository":{"id":157816545,"uuid":"140841652","full_name":"ahxt/NeuACF","owner":"ahxt","description":" Implementation of paper  Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks","archived":false,"fork":false,"pushed_at":"2019-06-05T17:12:09.000Z","size":76847,"stargazers_count":32,"open_issues_count":8,"forks_count":7,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-06T10:25:48.972Z","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/ahxt.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}},"created_at":"2018-07-13T11:58:18.000Z","updated_at":"2024-03-01T07:29:01.000Z","dependencies_parsed_at":"2023-10-20T16:26:13.160Z","dependency_job_id":null,"html_url":"https://github.com/ahxt/NeuACF","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ahxt/NeuACF","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahxt%2FNeuACF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahxt%2FNeuACF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahxt%2FNeuACF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahxt%2FNeuACF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ahxt","download_url":"https://codeload.github.com/ahxt/NeuACF/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahxt%2FNeuACF/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273381913,"owners_count":25095330,"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":"2025-09-03T02:00:09.631Z","response_time":76,"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":[],"created_at":"2024-12-04T17:25:10.970Z","updated_at":"2025-09-03T02:47:19.387Z","avatar_url":"https://github.com/ahxt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NeuACF\nThis is an implementation of paper  [(Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks)](https://www.ijcai.org/proceedings/2018/0471.pdf). \n\nPlease refer our paper if you use this code and the bibtex of this paper is:\n```\n@inproceedings{han2018aspect,\n   title={Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks.},\n   author=\"Han, Xiaotian and Shi, Chuan and Wang, Senzhang and Philip, S Yu and Song, Li\",\n   booktitle={IJCAI},\n   pages={3393--3399},\n   year={2018}\n }\n```\n\n\n### Requirements\n- Python 3.6\n- Tensorflow 1.2.1\n- docopt 0.6.2\n- numpy 1.13.3\n- sklearn 0.18.1\n- pandas 0.20.1\n- scipy 1.0.0\n\n### How to Run\n1. unzip dataset.7z\n2. Compute the aspect-level similarity matrix with the matlab code\n3. Run the model with the python code acf.py\n\nexample:\n```\n python ./acf.py ../dataset/amazon/ amovie --mat \"U.UIU,I.IUI,U.UICIU,I.ICI\" --epochs 40 --last_layer_size 64 --batch_size 1024 --num_of_neg 10 --learn_rate 0.00005 --num_of_layers 2 --mat_select median\n\n```\n\n### Parameters\n\nParameter | Note  \n|:---|:---|\n|--mat|sim_mat [default: \"\"]|\n|--epochs|Embedding size [default: 40]|\n|--last_layer_size| The number of iterations [default: 64]|\n|--num_of_layers|                The number of layers [default: 2]|\n|--num_of_neg|               The number of negs [default: 2]|\n|--learn_rate|                The learn_rate [default: 0.00005]|\n|--batch_size|                batch_size [default: 1024]|\n|--mat_select|                mat select type [default: median]|\n|--merge|                batch_size [default: attention]|\n\n\n#### Link\nFor more information, visit the webpage http://www.shichuan.org\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahxt%2Fneuacf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahxt%2Fneuacf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahxt%2Fneuacf/lists"}