{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/entire-space-multi-task-model-an-effective","title":"Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate","arxiv_id":"1804.07931","date":"2018-04-21","proceeding":null,"authors":["Xiao Ma","Liqin Zhao","Guan Huang","Zhi Wang","Zelin Hu","Xiaoqiang Zhu","Kun Gai"],"abstract":"Estimating post-click conversion rate (CVR) accurately is crucial for ranking\nsystems in industrial applications such as recommendation and advertising.\nConventional CVR modeling applies popular deep learning methods and achieves\nstate-of-the-art performance. However it encounters several task-specific\nproblems in practice, making CVR modeling challenging. For example,\nconventional CVR models are trained with samples of clicked impressions while\nutilized to make inference on the entire space with samples of all impressions.\nThis causes a sample selection bias problem. Besides, there exists an extreme\ndata sparsity problem, making the model fitting rather difficult. In this\npaper, we model CVR in a brand-new perspective by making good use of sequential\npattern of user actions, i.e., impression -> click -> conversion. The proposed\nEntire Space Multi-task Model (ESMM) can eliminate the two problems\nsimultaneously by i) modeling CVR directly over the entire space, ii) employing\na feature representation transfer learning strategy. Experiments on dataset\ngathered from Taobao's recommender system demonstrate that ESMM significantly\noutperforms competitive methods. We also release a sampling version of this\ndataset to enable future research. To the best of our knowledge, this is the\nfirst public dataset which contains samples with sequential dependence of click\nand conversion labels for CVR modeling.","url_abs":"http://arxiv.org/abs/1804.07931v2","url_pdf":"http://arxiv.org/pdf/1804.07931v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"entire-space-multi-task-model-an-effective","repo_url":"https://github.com/UlionTse/mlgb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"entire-space-multi-task-model-an-effective","repo_url":"https://github.com/shenweichen/DeepCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"entire-space-multi-task-model-an-effective","repo_url":"https://github.com/tangxyw/RecAlgorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"entire-space-multi-task-model-an-effective","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/multitask/esmm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"entire-space-multi-task-model-an-effective","repo_url":"https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/esmm.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"entire-space-multi-task-model-an-effective","repo_url":"https://github.com/shenweichen/DeepCTR-Torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"selection-bias","task_name":"Selection bias"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.07931","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}