{"id":13413363,"url":"https://github.com/timkaye11/goRecommend","last_synced_at":"2025-03-14T19:31:59.041Z","repository":{"id":18679299,"uuid":"21888151","full_name":"timkaye11/goRecommend","owner":"timkaye11","description":"Collaborative Filtering (CF) Algorithms in Go! ","archived":false,"fork":false,"pushed_at":"2014-07-29T04:49:57.000Z","size":272,"stargazers_count":206,"open_issues_count":0,"forks_count":23,"subscribers_count":11,"default_branch":"master","last_synced_at":"2024-10-25T05:23:16.510Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Go","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/timkaye11.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}},"created_at":"2014-07-16T05:32:23.000Z","updated_at":"2024-10-22T00:51:41.000Z","dependencies_parsed_at":"2022-07-10T07:46:06.037Z","dependency_job_id":null,"html_url":"https://github.com/timkaye11/goRecommend","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timkaye11%2FgoRecommend","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timkaye11%2FgoRecommend/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timkaye11%2FgoRecommend/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timkaye11%2FgoRecommend/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/timkaye11","download_url":"https://codeload.github.com/timkaye11/goRecommend/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243635367,"owners_count":20322927,"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":"2024-07-30T20:01:38.638Z","updated_at":"2025-03-14T19:31:59.008Z","avatar_url":"https://github.com/timkaye11.png","language":"Go","funding_links":[],"categories":["Machine Learning","机器学习","Relational Databases","\u003cspan id=\"机器学习-machine-learning\"\u003e机器学习 Machine Learning\u003c/span\u003e"],"sub_categories":["Advanced Console UIs","Search and Analytic Databases","检索及分析资料库","SQL 查询语句构建库","交流","\u003cspan id=\"高级控制台用户界面-advanced-console-uis\"\u003e高级控制台用户界面 Advanced Console UIs\u003c/span\u003e"],"readme":"## Go Recommend \n\n\u003e Recommendation algorithms (Collaborative Filtering) in Go! \n\n![](http://progressed.io/bar/100)\n\n### Background \nCollaborative Filtering (CF) is oftentimes used for item recommendations for users, and many libraries exist for other languages (popular implementations include Mahout, Prediction.IO, Apache MLLib ALS etc..). As there are very few machine learning packages out there for [Go](http://www.golang.org), I decided to put together some model based CF algorithms that I thought were interesting. \n\n---\n\n### Collaborative Filters inside this package. See each folder for examples/specifications\n\n- Alternating Least Squares (more info [here](http://labs.yahoo.com/files/HuKorenVolinsky-ICDM08.pdf) ) for both the Implicit and Explicit Case\n\t* Tests now complete\n\t* Use the implicit case for a confidence rating; explicit for predicting ratings\n- Simple Bayesian Collaborative Filtering Algorithm, see details [here](http://www-stat.wharton.upenn.edu/~edgeorge/Research_papers/Bcollab.pdf)\n\t* Tests complete\n- Similarity/Memory-based (using correlation, cosine and jaccard similarity) based CF, which incorporates a nearest neighbor type metric can be found in the CF folder.\n\t* Tests complete\n\t* See README for more details\n\t* Todo: consider approximate nearest neighbors algorithm. \n\n*Most* of the recommendation algorithms in this package are briefly outlined in [this article](http://www.hindawi.com/journals/aai/2009/421425/)\n\n---\n\n#### Additional\n\n - If you have any questions/comments, *please* feel free to reach me at tim [dot] kaye [at] lytics [dot] io\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimkaye11%2FgoRecommend","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimkaye11%2FgoRecommend","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimkaye11%2FgoRecommend/lists"}