{"id":19501596,"url":"https://github.com/vanessaaleung/ad-ctr-prediction","last_synced_at":"2026-04-13T14:32:54.006Z","repository":{"id":123120915,"uuid":"269745502","full_name":"vanessaaleung/ad-ctr-prediction","owner":"vanessaaleung","description":"Ads Click-Through-Rate Prediction","archived":false,"fork":false,"pushed_at":"2020-06-24T01:19:43.000Z","size":506,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-11-18T14:37:05.207Z","etag":null,"topics":["ctr","deep-learning","prediction","python","scikit-learn","spark"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/vanessaaleung.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":"2020-06-05T19:01:03.000Z","updated_at":"2025-11-10T01:33:18.000Z","dependencies_parsed_at":"2023-03-17T09:15:40.227Z","dependency_job_id":null,"html_url":"https://github.com/vanessaaleung/ad-ctr-prediction","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/vanessaaleung/ad-ctr-prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vanessaaleung%2Fad-ctr-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vanessaaleung%2Fad-ctr-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vanessaaleung%2Fad-ctr-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vanessaaleung%2Fad-ctr-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vanessaaleung","download_url":"https://codeload.github.com/vanessaaleung/ad-ctr-prediction/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vanessaaleung%2Fad-ctr-prediction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31757477,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-13T13:27:56.013Z","status":"ssl_error","status_checked_at":"2026-04-13T13:21:23.512Z","response_time":93,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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","deep-learning","prediction","python","scikit-learn","spark"],"created_at":"2024-11-10T22:13:23.984Z","updated_at":"2026-04-13T14:32:53.984Z","avatar_url":"https://github.com/vanessaaleung.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Ads Click-Through-Rate Prediction\nPredict ads click-through-rate on a user-ads category level. Check the presentation deck [here](https://docs.google.com/presentation/d/1bPIcwX8lUZLoGcVJVeiT4bYTSnznvrhjqSJ-daNgjMA/edit?usp=sharing).\n\n## **Data**\n[Ad Display/Click Data on Taobao.com](https://tianchi.aliyun.com/dataset/dataDetail?dataId=56)\n\nThis dataset is provided by Alimama and contains 1.14 million users behavior on Taobao.com platform.\n\n| Table| Description| Feature\n|---|---|---|\n|raw_sample|\traw training samples\t|User   ID, Ad ID, nonclk, clk, timestamp|\n|ad_feature|\tAd’s basic information\t|Ad   ID, campaign ID, Cate ID, Brand|\n|user_profile\t|user profile\t|User   ID, age, gender, etc|\n|raw_behavior_log\t|User behavior log\t|User   ID, btag, cate, brand, timestamp|\n\n## **Data Preprocessing**\nWe used BigQuery to sample 5 million users from the dataset and merge all the tables.\n\n## **Exploratory Data Analysis**\n\u003cimg src=\"eda.png\" width=\"700px\"\u003e\n\n## **Models**\n\u003cimg src=\"model_process.png\" width=\"700px\"\u003e\n\n- Logistic Regression, Random Forest with Sklearn\n- Logistic Regression, Random Forest with Spark MLlib\n- [Deep Interest Network Model](https://arxiv.org/pdf/1706.06978.pdf)\n\n**Deep Interest Network**\n\nIt introduces a local activation unit, with which the representation of user interests varies adaptively\ngiven different candidate ads.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanessaaleung%2Fad-ctr-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvanessaaleung%2Fad-ctr-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanessaaleung%2Fad-ctr-prediction/lists"}