{"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/learning-tree-based-deep-model-for","title":"Learning Tree-based Deep Model for Recommender Systems","arxiv_id":"1801.02294","date":"2018-01-08","proceeding":null,"authors":["Han Zhu","Xiang Li","Pengye Zhang","Guozheng Li","Jie He","Han Li","Kun Gai"],"abstract":"Model-based methods for recommender systems have been studied extensively in\nrecent years. In systems with large corpus, however, the calculation cost for\nthe learnt model to predict all user-item preferences is tremendous, which\nmakes full corpus retrieval extremely difficult. To overcome the calculation\nbarriers, models such as matrix factorization resort to inner product form\n(i.e., model user-item preference as the inner product of user, item latent\nfactors) and indexes to facilitate efficient approximate k-nearest neighbor\nsearches. However, it still remains challenging to incorporate more expressive\ninteraction forms between user and item features, e.g., interactions through\ndeep neural networks, because of the calculation cost.\n  In this paper, we focus on the problem of introducing arbitrary advanced\nmodels to recommender systems with large corpus. We propose a novel tree-based\nmethod which can provide logarithmic complexity w.r.t. corpus size even with\nmore expressive models such as deep neural networks. Our main idea is to\npredict user interests from coarse to fine by traversing tree nodes in a\ntop-down fashion and making decisions for each user-node pair. We also show\nthat the tree structure can be jointly learnt towards better compatibility with\nusers' interest distribution and hence facilitate both training and prediction.\nExperimental evaluations with two large-scale real-world datasets show that the\nproposed method significantly outperforms traditional methods. Online A/B test\nresults in Taobao display advertising platform also demonstrate the\neffectiveness of the proposed method in production environments.","url_abs":"http://arxiv.org/abs/1801.02294v5","url_pdf":"http://arxiv.org/pdf/1801.02294v5.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":"learning-tree-based-deep-model-for","repo_url":"https://github.com/alibaba/x-deeplearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"learning-tree-based-deep-model-for","repo_url":"https://github.com/baldandbrave/RecSysCOEN6313","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"learning-tree-based-deep-model-for","repo_url":"https://github.com/massquantity/dismember","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"learning-tree-based-deep-model-for","repo_url":"https://github.com/LRegan666/Tree_Deep_Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"learning-tree-based-deep-model-for","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/treebased/tdm/","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"learning-tree-based-deep-model-for","repo_url":"https://github.com/alibaba/TorchEasyRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.02294","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.02294"}},"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/alibaba/x-deeplearning","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/massquantity/dismember","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/LRegan666/Tree_Deep_Model","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/alibaba/TorchEasyRec","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/treebased/tdm/","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/baldandbrave/RecSysCOEN6313","reach":{"status":"unanswered"}}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"7371743fce1a0a82","entry":"candidates_generator","repo":"LRegan666/Tree_Deep_Model","repo_kind":"listed","path":"prediction.py","file_url":"https://github.com/LRegan666/Tree_Deep_Model/blob/HEAD/prediction.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7371743fce1a0a82"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}