{"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/warm-up-cold-start-advertisements-improving","title":"Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings","arxiv_id":"1904.11547","date":"2019-04-25","proceeding":null,"authors":["Feiyang Pan","Shuokai Li","Xiang Ao","Pingzhong Tang","Qing He"],"abstract":"Click-through rate (CTR) prediction has been one of the most central problems\nin computational advertising. Lately, embedding techniques that produce\nlow-dimensional representations of ad IDs drastically improve CTR prediction\naccuracies. However, such learning techniques are data demanding and work\npoorly on new ads with little logging data, which is known as the cold-start\nproblem.\n  In this paper, we aim to improve CTR predictions during both the cold-start\nphase and the warm-up phase when a new ad is added to the candidate pool. We\npropose Meta-Embedding, a meta-learning-based approach that learns to generate\ndesirable initial embeddings for new ad IDs. The proposed method trains an\nembedding generator for new ad IDs by making use of previously learned ads\nthrough gradient-based meta-learning. In other words, our method learns how to\nlearn better embeddings. When a new ad comes, the trained generator initializes\nthe embedding of its ID by feeding its contents and attributes. Next, the\ngenerated embedding can speed up the model fitting during the warm-up phase\nwhen a few labeled examples are available, compared to the existing\ninitialization methods.\n  Experimental results on three real-world datasets showed that Meta-Embedding\ncan significantly improve both the cold-start and warm-up performances for six\nexisting CTR prediction models, ranging from lightweight models such as\nFactorization Machines to complicated deep models such as PNN and DeepFM. All\nof the above apply to conversion rate (CVR) predictions as well.","url_abs":"http://arxiv.org/abs/1904.11547v1","url_pdf":"http://arxiv.org/pdf/1904.11547v1.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":"warm-up-cold-start-advertisements-improving","repo_url":"https://github.com/Feiyang/MetaEmbedding","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.11547","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}