{"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/fibinet-combining-feature-importance-and","title":"FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction","arxiv_id":"1905.09433","date":"2019-05-23","proceeding":null,"authors":["Tongwen Huang","Zhiqi Zhang","Junlin Zhang"],"abstract":"Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).","url_abs":"https://arxiv.org/abs/1905.09433v1","url_pdf":"https://arxiv.org/pdf/1905.09433v1.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":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/HaSai666/rec_pangu","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/Hirosora/LightCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/Prayforhanluo/CTR_Algorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","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":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/YinzhenWan/recome_wan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/ptzhangAlg/RecAlg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/recommendation-algorithm/fibinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/shenweichen/DeepCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/shenweichen/DeepCTR-Torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fibinet-combining-feature-importance-and","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":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/xue-pai/FuxiCTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/2023-MindSpore-1/ms-code-220/tree/main/fibinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/DSXiangLi/CTR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/DataCanvasIO/DeepTables","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/QunBB/DeepLearning/blob/main/Recommendation/RANK/fibinet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/RUCAIBox/EulerNet/blob/main/Model/Baselines/FiBiNet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/anonctr/GDCN/blob/main/models/FiBiNet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/bruce-willis/FiBiNET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/codectr/RefineCTR/blob/main/FRCTR/model_zoo/fibinet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/datawhalechina/fun-rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/datawhalechina/torch-rechub/blob/main/torch_rechub/models/ranking/fibinet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/huangjunheng/recommendation_model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/iFe1er/AlitaNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/lyhue1991/eat_pytorch_in_20_days","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/recommend/fibinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/p768lwy3/torecsys/blob/master/torecsys/models/ctr/feature_importance_and_bilinear_feature_interaction_network.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/qiaoguan/deep-ctr-prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/whw199833/gbiz_torch/blob/main/example/model/FiBiNetModel_test.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/xiuyu0000/new_papers_codes/tree/main/fibinet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fibinet-combining-feature-importance-and","repo_url":"https://github.com/zhongqiangwu960812/AI-RecommenderSystem","is_official":0,"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":"feature-importance","task_name":"Feature Importance"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/click-through-rate-prediction-on-criteo","task":"Click-Through Rate Prediction","dataset":"Criteo","model":"FiBiNET","rank_in_archive_order":21,"of":39,"metrics":{"AUC":"0.8103","Log Loss":"0.4423"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.09433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.09433"}},"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/lyhue1991/eat_pytorch_in_20_days","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/codectr/RefineCTR/blob/main/FRCTR/model_zoo/fibinet.py","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xiuyu0000/new_papers_codes/tree/main/fibinet","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/anonctr/GDCN/blob/main/models/FiBiNet.py","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/YinzhenWan/recome_wan","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/qiaoguan/deep-ctr-prediction","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xue-pai/FuxiCTR","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ptzhangAlg/RecAlg","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/iFe1er/AlitaNet","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/datawhalechina/torch-rechub/blob/main/torch_rechub/models/ranking/fibinet.py","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/shenweichen/DeepCTR","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/recommendation-algorithm/fibinet","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/p768lwy3/torecsys/blob/master/torecsys/models/ctr/feature_importance_and_bilinear_feature_interaction_network.py","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mindspore-ai/models/tree/master/research/recommend/fibinet","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Prayforhanluo/CTR_Algorithm","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/RUCAIBox/EulerNet/blob/main/Model/Baselines/FiBiNet.py","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/datawhalechina/fun-rec","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/zhongqiangwu960812/AI-RecommenderSystem","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/UlionTse/mlgb","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/DataCanvasIO/DeepTables","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/huangjunheng/recommendation_model","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/QunBB/DeepLearning/blob/main/Recommendation/RANK/fibinet.py","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/shenweichen/DeepCTR-Torch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tangxyw/RecAlgorithm","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"summary":{"ran_honours":2,"unverified":24},"by_repo_kind":{"listed":{"samples":26,"ran":2,"repositories":5}},"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":3,"samples":[{"code_sha256_prefix":"c7366824116a54de","entry":"count_params","repo":"codectr/RefineCTR","repo_kind":"listed","path":"evaluation/mains/main_criteo_base.py","file_url":"https://github.com/codectr/RefineCTR/blob/HEAD/evaluation/mains/main_criteo_base.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c7366824116a54de"}},{"code_sha256_prefix":"e2dddebbea9ec26d","entry":"train","repo":"codectr/RefineCTR","repo_kind":"listed","path":"evaluation/mains/main_criteo_base.py","file_url":"https://github.com/codectr/RefineCTR/blob/HEAD/evaluation/mains/main_criteo_base.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e2dddebbea9ec26d"}},{"code_sha256_prefix":"b9cfbe7def435449","entry":"activation_layer","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/basic/activation.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/basic/activation.py","link_basis":"harvester_set","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":"b9cfbe7def435449"}},{"code_sha256_prefix":"c0a4eb57bcd0b82c","entry":"afm_model_fn","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/AFM/afm.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/AFM/afm.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"c0a4eb57bcd0b82c"}},{"code_sha256_prefix":"f595ec56e4f6d96c","entry":"auc_score","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/basic/metric.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/basic/metric.py","link_basis":"harvester_set","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":"f595ec56e4f6d96c"}},{"code_sha256_prefix":"655674e1dd763c2b","entry":"bidirectional_dynamic_rnn","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/DIEN/rnn.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/DIEN/rnn.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"655674e1dd763c2b"}},{"code_sha256_prefix":"8d782cde176050f5","entry":"columns_info","repo":"DataCanvasIO/DeepTables","repo_kind":"listed","path":"deeptables/eda/utils.py","file_url":"https://github.com/DataCanvasIO/DeepTables/blob/HEAD/deeptables/eda/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8d782cde176050f5"}},{"code_sha256_prefix":"6ab7bf854f4e2b6a","entry":"cross_layer","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/DCN/cross_layer.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/DCN/cross_layer.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"6ab7bf854f4e2b6a"}},{"code_sha256_prefix":"cf507ada9edcace1","entry":"dice","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/DIN/activations.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/DIN/activations.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"cf507ada9edcace1"}},{"code_sha256_prefix":"e9998a77e293fcee","entry":"dynamic_rnn","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/DIEN/rnn.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/DIEN/rnn.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"e9998a77e293fcee"}},{"code_sha256_prefix":"cffd7b17cdcd0cf1","entry":"eval_input_fn","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/utils.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"cffd7b17cdcd0cf1"}},{"code_sha256_prefix":"e123b326d5597e1a","entry":"example_parser","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/AFM/afm.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/AFM/afm.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"e123b326d5597e1a"}},{"code_sha256_prefix":"b05d0a65616555c4","entry":"extract_feature_info","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/utils/model_utils.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/utils/model_utils.py","link_basis":"harvester_set","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":"b05d0a65616555c4"}},{"code_sha256_prefix":"8a2d833b56e533eb","entry":"gauc_score","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/basic/metric.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/basic/metric.py","link_basis":"harvester_set","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":"8a2d833b56e533eb"}},{"code_sha256_prefix":"39e31791691af4ad","entry":"generate_dummy_input","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/utils/model_utils.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/utils/model_utils.py","link_basis":"harvester_set","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":"39e31791691af4ad"}},{"code_sha256_prefix":"bd1115d11db962ea","entry":"generate_dummy_input_dict","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/utils/model_utils.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/utils/model_utils.py","link_basis":"harvester_set","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":"bd1115d11db962ea"}},{"code_sha256_prefix":"d1f034d725053a85","entry":"get_auc","repo":"huangjunheng/recommendation_model","repo_kind":"listed","path":"FiBiNET/FibiNET.py","file_url":"https://github.com/huangjunheng/recommendation_model/blob/HEAD/FiBiNET/FibiNET.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d1f034d725053a85"}},{"code_sha256_prefix":"073e23c27ccb1369","entry":"get_user_pred","repo":"datawhalechina/torch-rechub","repo_kind":"listed","path":"torch_rechub/basic/metric.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/basic/metric.py","link_basis":"harvester_set","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":"073e23c27ccb1369"}},{"code_sha256_prefix":"a454c87f745b2bc3","entry":"leakyrelu","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/BST/leakyrelu.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/BST/leakyrelu.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"a454c87f745b2bc3"}},{"code_sha256_prefix":"acec900be1840259","entry":"mini_dt_space_validator","repo":"DataCanvasIO/DeepTables","repo_kind":"listed","path":"deeptables/models/hyper_dt.py","file_url":"https://github.com/DataCanvasIO/DeepTables/blob/HEAD/deeptables/models/hyper_dt.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"acec900be1840259"}},{"code_sha256_prefix":"3e2819d0a6e96a88","entry":"prelu","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/DIN/activations.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/DIN/activations.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3e2819d0a6e96a88"}},{"code_sha256_prefix":"d657922c4bdcd359","entry":"raw_rnn","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/DIEN/rnn.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/DIEN/rnn.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"d657922c4bdcd359"}},{"code_sha256_prefix":"24e398d27e643ab0","entry":"target_rate_encodeing","repo":"DataCanvasIO/DeepTables","repo_kind":"listed","path":"deeptables/preprocessing/utils.py","file_url":"https://github.com/DataCanvasIO/DeepTables/blob/HEAD/deeptables/preprocessing/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"24e398d27e643ab0"}},{"code_sha256_prefix":"edc3a0e6c82606b3","entry":"to_sparse_tensor","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/utils.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"edc3a0e6c82606b3"}},{"code_sha256_prefix":"a472efb327506298","entry":"top_categories","repo":"DataCanvasIO/DeepTables","repo_kind":"listed","path":"deeptables/eda/utils.py","file_url":"https://github.com/DataCanvasIO/DeepTables/blob/HEAD/deeptables/eda/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a472efb327506298"}},{"code_sha256_prefix":"35705e483fa1b7a1","entry":"train_input_fn","repo":"tangxyw/RecAlgorithm","repo_kind":"listed","path":"algorithm/utils.py","file_url":"https://github.com/tangxyw/RecAlgorithm/blob/HEAD/algorithm/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"35705e483fa1b7a1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}