{"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/practice-on-long-sequential-user-behavior","title":"Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction","arxiv_id":"1905.09248","date":"2019-05-22","proceeding":null,"authors":["Qi Pi","Weijie Bian","Guorui Zhou","Xiaoqiang Zhu","Kun Gai"],"abstract":"Click-through rate (CTR) prediction is critical for industrial applications such as recommender system and online advertising. Practically, it plays an important role for CTR modeling in these applications by mining user interest from rich historical behavior data. Driven by the development of deep learning, deep CTR models with ingeniously designed architecture for user interest modeling have been proposed, bringing remarkable improvement of model performance over offline metric.However, great efforts are needed to deploy these complex models to online serving system for realtime inference, facing massive traffic request. Things turn to be more difficult when it comes to long sequential user behavior data, as the system latency and storage cost increase approximately linearly with the length of user behavior sequence. In this paper, we face directly the challenge of long sequential user behavior modeling and introduce our hands-on practice with the co-design of machine learning algorithm and online serving system for CTR prediction task. Theoretically, the co-design solution of UIC and MIMN enables us to handle the user interest modeling with unlimited length of sequential behavior data. Comparison between model performance and system efficiency proves the effectiveness of proposed solution. To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands. It now has been deployed in the display advertising system in Alibaba.","url_abs":"https://arxiv.org/abs/1905.09248v3","url_pdf":"https://arxiv.org/pdf/1905.09248v3.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":"practice-on-long-sequential-user-behavior","repo_url":"https://github.com/UIC-Paper/MIMN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"practice-on-long-sequential-user-behavior","repo_url":"https://github.com/xiaominglalala/Session_based_Recommendation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"ok"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.09248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.09248"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/UIC-Paper/MIMN","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xiaominglalala/Session_based_Recommendation","reach":{"status":"ok"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"188a7c5e6d562ed9","entry":"create_linear_initializer","repo":"UIC-Paper/MIMN","repo_kind":"official","path":"script/mimn.py","file_url":"https://github.com/UIC-Paper/MIMN/blob/HEAD/script/mimn.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"188a7c5e6d562ed9"}},{"code_sha256_prefix":"b1f1b3e24df9c245","entry":"dice","repo":"UIC-Paper/MIMN","repo_kind":"official","path":"script/Dice.py","file_url":"https://github.com/UIC-Paper/MIMN/blob/HEAD/script/Dice.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b1f1b3e24df9c245"}},{"code_sha256_prefix":"3d130d4e1df6bced","entry":"expand","repo":"UIC-Paper/MIMN","repo_kind":"official","path":"script/mimn.py","file_url":"https://github.com/UIC-Paper/MIMN/blob/HEAD/script/mimn.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3d130d4e1df6bced"}},{"code_sha256_prefix":"4b7fd24acebe45e5","entry":"learned_init","repo":"UIC-Paper/MIMN","repo_kind":"official","path":"script/mimn.py","file_url":"https://github.com/UIC-Paper/MIMN/blob/HEAD/script/mimn.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4b7fd24acebe45e5"}},{"code_sha256_prefix":"9a523e5bd9c79de4","entry":"parametric_relu","repo":"UIC-Paper/MIMN","repo_kind":"official","path":"script/Dice.py","file_url":"https://github.com/UIC-Paper/MIMN/blob/HEAD/script/Dice.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9a523e5bd9c79de4"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}