{"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/recurrent-meta-learning-against-generalized","title":"Recurrent Meta-Learning against Generalized Cold-start Problem in CTR Prediction","arxiv_id":null,"date":"2022-10-01","proceeding":"Conference 2022 10","authors":["Junyu Chen","Qianqian Xu","Zhiyong Yang","Ke Ma","Xiaochun Cao","Qingming Huang"],"abstract":"During the last decades, great success has been witnessed along the course of accurate Click-Through-Rate (CTR) prediction models for online advertising. However, the cold-start problem, which refers to the issue that the standard models can hardly draw accurate inferences for unseen users/ads, is still yet to be fully understood. Most recently, some related studies have been proposed to tackle this problem with only the new users/ads being considered. We argue that such new users/ads are not the only sources for cold-start. From another perspective, since users might shift their interests over time, one's recent behaviors might vary greatly from the records long ago. In this sense, we believe that the cold-start problem should also exist along the temporal dimension. Motivated by this, a generalized definition of the cold-start problem is provided where both new users/ads and recent behavioral data from known users are considered. To attack this problem, we propose a recursive meta-learning model with the user's behavior sequence prediction as a separate training task. Specifically, a time-series CTR model with the MAML (Model-Agnostic Meta-Learning)-like meta-learning method is proposed to make our model adapt to new tasks rapidly. Besides, we propose a parallel structure for extracting the feature interactions to efficiently fuse attention mechanisms and the RNN layer. Finally, experiments on three public datasets demonstrate the effectiveness of the proposed approaches.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3503161.3548118","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3503161.3548118","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":"recurrent-meta-learning-against-generalized","repo_url":"https://github.com/2023-MindSpore-4/Code16/tree/main/yangzhiyong/recurrent_-meta_-learning_for_-ctr_mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}