{"id":13906457,"url":"https://github.com/caserec/CaseRecommender","last_synced_at":"2025-07-18T04:31:29.372Z","repository":{"id":37405774,"uuid":"46072088","full_name":"caserec/CaseRecommender","owner":"caserec","description":"Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems","archived":false,"fork":false,"pushed_at":"2024-01-10T20:36:33.000Z","size":1418,"stargazers_count":485,"open_issues_count":6,"forks_count":91,"subscribers_count":22,"default_branch":"master","last_synced_at":"2024-11-07T08:08:58.737Z","etag":null,"topics":["evaluation","python","ranking","rating-prediction","recommendation-system","recommender-systems","top-k"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/caserec.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"COPYING","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-11-12T18:25:39.000Z","updated_at":"2024-11-06T03:55:10.000Z","dependencies_parsed_at":"2022-07-08T17:47:57.201Z","dependency_job_id":null,"html_url":"https://github.com/caserec/CaseRecommender","commit_stats":null,"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caserec%2FCaseRecommender","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caserec%2FCaseRecommender/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caserec%2FCaseRecommender/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caserec%2FCaseRecommender/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/caserec","download_url":"https://codeload.github.com/caserec/CaseRecommender/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226344644,"owners_count":17610180,"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":["evaluation","python","ranking","rating-prediction","recommendation-system","recommender-systems","top-k"],"created_at":"2024-08-06T23:01:36.197Z","updated_at":"2024-11-25T14:31:33.405Z","avatar_url":"https://github.com/caserec.png","language":"Python","funding_links":[],"categories":["推荐系统","推荐系统算法库与列表","5. GitHub Repositories"],"sub_categories":["网络服务_其他"],"readme":"# Case Recommender - A Python Framework for RecSys\n\n[![PyPI version](https://badge.fury.io/py/CaseRecommender.svg)](https://badge.fury.io/py/CaseRecommender)\n[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)\n[![GitHub license](https://img.shields.io/github/license/caserec/CaseRecommender.svg)](https://github.com/caserec/CaseRecommender/blob/master/COPYING)\n\nCase Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback. The framework aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating prediction approaches, and different metrics validation and evaluation.\n\n# Algorithms\n\nItem Recommendation:\n\n- BPRMF\n\n- ItemKNN\n\n- Item Attribute KNN\n\n- UserKNN\n\n- User Attribute KNN\n\n- Group-based (Clustering-based algorithm)\n\n- Paco Recommender (Co-Clustering-based algorithm)\n\n- Most Popular\n\n- Random\n\n- Content Based\n\nRating Prediction:\n\n- Matrix Factorization (with and without baseline)\n\n- Non-negative Matrix Factorization\n\n- SVD\n\n- SVD++\n\n- ItemKNN\n\n- Item Attribute KNN\n\n- UserKNN\n\n- User Attribute KNN\n\n- Item NSVD1 (with and without Batch)\n\n- User NSVD1 (with and without Batch)\n\n- Most Popular\n\n- Random\n\n- gSVD++\n\n- Item-MSMF\n\n- (E) CoRec\n\nClustering:\n\n- PaCo: EntroPy Anomalies in Co-Clustering\n\n- k-medoids\n\n# Evaluation and Validation Metrics\n\n- All-but-one Protocol\n\n- Cross-fold-Validation\n\n- Item Recommendation: Precision, Recall, NDCG and Map\n\n- Rating Prediction: MAE and RMSE\n\n- Statistical Analysis (T-test and Wilcoxon)\n\n# Requirements\n\n- Python\n- scipy\n- numpy\n- pandas\n- scikit-learn\n\nFor Linux and MAC use:\n\n    $ pip install requirements\n\nFor Windows use:\n\n    http://www.lfd.uci.edu/~gohlke/pythonlibs/\n\n# Installation\n\nCase Recommender can be installed using pip:\n\n    $ pip install caserecommender\n\nIf you want to run the latest version of the code, you can install from git:\n\n    $ pip install -U git+git://github.com/caserec/CaseRecommender.git\n\n# Quick Start and Guide\n\nFor more information about RiVal and the documentation, visit the Case Recommender [Wiki](https://github.com/caserec/CaseRecommender/wiki). If you have not used Case Recommender before, do check out the Getting Started guide.\n\n# Usage\n\nDivide Database (Fold Cross Validation)\n\n    \u003e\u003e from caserec.utils.split_database import SplitDatabase\n    \u003e\u003e SplitDatabase(input_file=dataset, dir_folds=dir_path, n_splits=10).k_fold_cross_validation()\n\nRun Item Recommendation Algorithm (E.g: ItemKNN)\n\n    \u003e\u003e from caserec.recommenders.item_recommendation.itemknn import ItemKNN\n    \u003e\u003e ItemKNN(train_file, test_file).compute()\n\nRun Rating Prediction Algorithm (E.g: ItemKNN)\n\n    \u003e\u003e from caserec.recommenders.rating_prediction.itemknn import ItemKNN\n    \u003e\u003e ItemKNN(train_file, test_file).compute()\n\nEvaluate Ranking (Prec@N, Recall@N, NDCG@, Map@N and Map Total)\n\n    \u003e\u003e from caserec.evaluation.item_recommendation import ItemRecommendationEvaluation\n    \u003e\u003e ItemRecommendationEvaluation().evaluate_with_files(predictions_file, test_file)\n\nEvaluate Ranking (MAE and RMSE)\n\n    \u003e\u003e from caserec.evaluation.rating_prediction import RatingPredictionEvaluation\n    \u003e\u003e RatingPredictionEvaluation().evaluate_with_files(predictions_file, test_file)\n\n# Input\n\nThe input-files of traditional have to be placed in the corresponding subdirectory and are in csv-format with at least\n3 columns. Example: user_1,item_1,feedback\n\n# Cite us\n\nIf you use Case Recommender in a scientific publication, we would appreciate citations of our paper where this framework was first mentioned and used.\n\nTo cite Case Recommender use: Arthur da Costa, Eduardo Fressato, Fernando Neto, Marcelo Manzato, and Ricardo Campello. 2019. Case recommender: a flexible and extensible python framework for recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18). ACM, New York, NY, USA, 494-495. DOI: https://doi.org/10.1145/3240323.3241611.\n\nFor TeX/LaTeX (BibTex):\n\n        @inproceedings{daCosta:2018:CRF:3240323.3241611,\n            author = {da Costa, Arthur and Fressato, Eduardo and Neto, Fernando and Manzato, Marcelo and Campello, Ricardo},\n            title = {Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems},\n            booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems},\n            series = {RecSys '18},\n            year = {2018},\n            isbn = {978-1-4503-5901-6},\n            location = {Vancouver, British Columbia, Canada},\n            pages = {494--495},\n            numpages = {2},\n            url = {http://doi.acm.org/10.1145/3240323.3241611},\n            doi = {10.1145/3240323.3241611},\n            acmid = {3241611},\n            publisher = {ACM},\n            address = {New York, NY, USA},\n            keywords = {framework, python, recommender systems},\n        }\n\n# Help CaseRecommender\n\nTo help the project with contributions follow the steps:\n\n- Fork CaseRecommender\n\n- Make your alterations and commit\n\n- Create a topic branch - git checkout -b my_branch\n\n- Push to your branch - git push origin my_branch\n\n- Create a Pull Request from your branch.\n\n- You just contributed to the CaseRecommender project!\n\nFor bugs or feedback use this link: https://github.com/caserec/CaseRecommender/issues\n\n# License (MIT)\n\n    © 2019. Case Recommender All Rights Reserved\n\n    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n    documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the\n    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to\n    permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\n    The above copyright notice and this permission notice shall be included in all copies or substantial portions of\n    the Software.\n\n    THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO\n    THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\n    TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n    IN THE SOFTWARE.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaserec%2FCaseRecommender","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcaserec%2FCaseRecommender","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaserec%2FCaseRecommender/lists"}