{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/click-through-rate-prediction/papers/2","list_of":"/task/click-through-rate-prediction","task":"Click-Through Rate Prediction","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":4,"rows_per_page":100,"rows":[101,200],"of":391,"counts":{"archive_papers_tagged":391,"with_a_code_link":165,"where_syntology_ran_a_sample":25,"not_listed_spam_title":0,"listed":391,"listed_where_code_ran":25,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/click-through-rate-prediction","prev":"/task/click-through-rate-prediction","next":"/task/click-through-rate-prediction/papers/3","papers":[{"url":"/paper/outrank-speeding-up-automl-based-model-search","slug":"outrank-speeding-up-automl-based-model-search","title":"OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking","date":"2023-09-04","arxiv_id":"2309.01552","repositories_listed":1,"syntology":null},{"url":"/paper/map-a-model-agnostic-pretraining-framework","slug":"map-a-model-agnostic-pretraining-framework","title":"MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction","date":"2023-08-03","arxiv_id":"2308.01737","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/map-a-model-agnostic-pretraining-framework#ran","syntology_url":"https://syntology.ai/paper/2308.01737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01737"}},"official":{"repos":["chiangel/map-code"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weighted-multi-level-feature-factorization","slug":"weighted-multi-level-feature-factorization","title":"Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction","date":"2023-08-03","arxiv_id":"2308.02568","repositories_listed":1,"syntology":null},{"url":"/paper/courier-contrastive-user-intention","slug":"courier-contrastive-user-intention","title":"COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation","date":"2023-06-08","arxiv_id":"2306.05001","repositories_listed":1,"syntology":null},{"url":"/paper/reformulating-ctr-prediction-learning","slug":"reformulating-ctr-prediction-learning","title":"Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation","date":"2023-04-26","arxiv_id":"2304.13643","repositories_listed":1,"syntology":null},{"url":"/paper/fan-fatigue-aware-network-for-click-through","slug":"fan-fatigue-aware-network-for-click-through","title":"FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation","date":"2023-04-10","arxiv_id":"2304.04529","repositories_listed":1,"syntology":null},{"url":"/paper/the-re-label-method-for-data-centric-machine","slug":"the-re-label-method-for-data-centric-machine","title":"The Re-Label Method For Data-Centric Machine Learning","date":"2023-02-09","arxiv_id":"2302.04391","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-feature-set-for-click-through-rate","slug":"optimizing-feature-set-for-click-through-rate","title":"Optimizing Feature Set for Click-Through Rate Prediction","date":"2023-01-26","arxiv_id":"2301.10909","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/optimizing-feature-set-for-click-through-rate#ran","syntology_url":"https://syntology.ai/paper/2301.10909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10909"}},"official":{"repos":["fuyuanlyu/optfs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/xdeepint-a-hybrid-architecture-for-modeling","slug":"xdeepint-a-hybrid-architecture-for-modeling","title":"xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions","date":"2023-01-03","arxiv_id":"2301.01089","repositories_listed":1,"syntology":null},{"url":"/paper/cl4ctr-a-contrastive-learning-framework-for","slug":"cl4ctr-a-contrastive-learning-framework-for","title":"CL4CTR: A Contrastive Learning Framework for CTR Prediction","date":"2022-12-01","arxiv_id":"2212.00522","repositories_listed":1,"syntology":null},{"url":"/paper/directed-acyclic-graph-factorization-machines","slug":"directed-acyclic-graph-factorization-machines","title":"Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation","date":"2022-11-21","arxiv_id":"2211.11159","repositories_listed":1,"syntology":null},{"url":"/paper/resus-warm-up-cold-users-via-meta-learning","slug":"resus-warm-up-cold-users-via-meta-learning","title":"RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction","date":"2022-10-28","arxiv_id":"2210.16080","repositories_listed":1,"syntology":null},{"url":"/paper/memonet-memorizing-representations-of-all","slug":"memonet-memorizing-representations-of-all","title":"MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction","date":"2022-10-25","arxiv_id":"2211.01334","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multi-representation-model-for-click","slug":"deep-multi-representation-model-for-click","title":"Deep Multi-Representation Model for Click-Through Rate Prediction","date":"2022-10-18","arxiv_id":"2210.10664","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-embedding-tables-without-first","slug":"clustering-embedding-tables-without-first","title":"Clustering the Sketch: A Novel Approach to Embedding Table Compression","date":"2022-10-12","arxiv_id":"2210.05974","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-deep-ctr-prediction-with-a-plug-and","slug":"boosting-deep-ctr-prediction-with-a-plug-and","title":"Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-meta-learning-against-generalized","slug":"recurrent-meta-learning-against-generalized","title":"Recurrent Meta-Learning against Generalized Cold-start Problem in CTR Prediction","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/feature-embedding-in-click-through-rate","slug":"feature-embedding-in-click-through-rate","title":"Feature embedding in click-through rate prediction","date":"2022-09-20","arxiv_id":"2209.09481","repositories_listed":1,"syntology":null},{"url":"/paper/towards-understanding-the-overfitting","slug":"towards-understanding-the-overfitting","title":"Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models","date":"2022-09-04","arxiv_id":"2209.06053","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-in-recommender-systems-a","slug":"causal-inference-in-recommender-systems-a","title":"Causal Inference in Recommender Systems: A Survey and Future Directions","date":"2022-08-26","arxiv_id":"2208.12397","repositories_listed":1,"syntology":null},{"url":"/paper/joint-optimization-of-ranking-and-calibration","slug":"joint-optimization-of-ranking-and-calibration","title":"Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model","date":"2022-08-12","arxiv_id":"2208.06164","repositories_listed":1,"syntology":null},{"url":"/paper/optembed-learning-optimal-embedding-table-for","slug":"optembed-learning-optimal-embedding-table-for","title":"OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction","date":"2022-08-09","arxiv_id":"2208.04482","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/optembed-learning-optimal-embedding-table-for#ran","syntology_url":"https://syntology.ai/paper/2208.04482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.04482"}},"official":{"repos":["fuyuanlyu/optembed"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/sparse-attentive-memory-network-for-click","slug":"sparse-attentive-memory-network-for-click","title":"Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences","date":"2022-08-08","arxiv_id":"2208.04022","repositories_listed":1,"syntology":null},{"url":"/paper/lpfs-learnable-polarizing-feature-selection","slug":"lpfs-learnable-polarizing-feature-selection","title":"LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction","date":"2022-06-01","arxiv_id":"2206.00267","repositories_listed":1,"syntology":null},{"url":"/paper/sampling-is-all-you-need-on-modeling-long","slug":"sampling-is-all-you-need-on-modeling-long","title":"Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction","date":"2022-05-20","arxiv_id":"2205.10249","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-ctr-prediction-with-context-aware","slug":"enhancing-ctr-prediction-with-context-aware","title":"Enhancing CTR Prediction with Context-Aware Feature Representation Learning","date":"2022-04-19","arxiv_id":"2204.08758","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-ctr-prediction-with-context-aware#ran","syntology_url":"https://syntology.ai/paper/2204.08758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08758"}},"official":{"repos":["frnetnetwork/frnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/cowclip-reducing-ctr-prediction-model","slug":"cowclip-reducing-ctr-prediction-model","title":"CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU","date":"2022-04-13","arxiv_id":"2204.06240","repositories_listed":1,"syntology":null},{"url":"/paper/i-razor-a-neural-input-razor-for-feature","slug":"i-razor-a-neural-input-razor-for-feature","title":"i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems","date":"2022-04-01","arxiv_id":"2204.00281","repositories_listed":1,"syntology":null},{"url":"/paper/apg-adaptive-parameter-generation-network-for","slug":"apg-adaptive-parameter-generation-network-for","title":"APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction","date":"2022-03-30","arxiv_id":"2203.16218","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/apg-adaptive-parameter-generation-network-for#ran","syntology_url":"https://syntology.ai/paper/2203.16218","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16218"}},"official":null}},{"url":"/paper/modeling-users-contextualized-page-wise","slug":"modeling-users-contextualized-page-wise","title":"Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search","date":"2022-03-29","arxiv_id":"2203.15542","repositories_listed":1,"syntology":null},{"url":"/paper/gift-graph-guided-feature-transfer-for-cold","slug":"gift-graph-guided-feature-transfer-for-cold","title":"GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction","date":"2022-02-21","arxiv_id":"2202.11525","repositories_listed":1,"syntology":null},{"url":"/paper/triangle-graph-interest-network-for-click","slug":"triangle-graph-interest-network-for-click","title":"Triangle Graph Interest Network for Click-through Rate Prediction","date":"2022-02-06","arxiv_id":"2202.02698","repositories_listed":1,"syntology":null},{"url":"/paper/deep-interest-highlight-network-for-click","slug":"deep-interest-highlight-network-for-click","title":"Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation","date":"2022-02-05","arxiv_id":"2202.08959","repositories_listed":1,"syntology":null},{"url":"/paper/same-scenario-adaptive-mixture-of-experts-for","slug":"same-scenario-adaptive-mixture-of-experts-for","title":"MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction","date":"2021-12-27","arxiv_id":"2112.13747","repositories_listed":1,"syntology":null},{"url":"/paper/paddlerec","slug":"paddlerec","title":"PaddleRec","date":"2021-11-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/aim-automatic-interaction-machine-for-click","slug":"aim-automatic-interaction-machine-for-click","title":"AIM: Automatic Interaction Machine for Click-Through Rate Prediction","date":"2021-11-05","arxiv_id":"2111.03318","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-nas-framework-and-application","slug":"differentiable-nas-framework-and-application","title":"Differentiable NAS Framework and Application to Ads CTR Prediction","date":"2021-10-25","arxiv_id":"2110.14812","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-interaction-machine-for-tabular","slug":"retrieval-interaction-machine-for-tabular","title":"Retrieval & Interaction Machine for Tabular Data Prediction","date":"2021-08-11","arxiv_id":"2108.05252","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-user-behavior-retrieval-in-click","slug":"end-to-end-user-behavior-retrieval-in-click","title":"End-to-End User Behavior Retrieval in Click-Through RatePrediction Model","date":"2021-08-10","arxiv_id":"2108.04468","repositories_listed":1,"syntology":null},{"url":"/paper/memorize-factorize-or-be-naive-learning","slug":"memorize-factorize-or-be-naive-learning","title":"Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction","date":"2021-08-03","arxiv_id":"2108.01265","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/memorize-factorize-or-be-naive-learning#ran","syntology_url":"https://syntology.ai/paper/2108.01265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.01265"}},"official":{"repos":["fuyuanlyu/OptInter"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/fint-field-aware-interaction-neural-network","slug":"fint-field-aware-interaction-neural-network","title":"FINT: Field-aware INTeraction Neural Network For CTR Prediction","date":"2021-07-05","arxiv_id":"2107.01999","repositories_listed":1,"syntology":null},{"url":"/paper/deep-position-wise-interaction-network-for","slug":"deep-position-wise-interaction-network-for","title":"Deep Position-wise Interaction Network for CTR Prediction","date":"2021-06-10","arxiv_id":"2106.05482","repositories_listed":1,"syntology":null},{"url":"/paper/dual-attentive-sequential-learning-for-cross","slug":"dual-attentive-sequential-learning-for-cross","title":"Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction","date":"2021-06-05","arxiv_id":"2106.02768","repositories_listed":1,"syntology":null},{"url":"/paper/learning-graph-meta-embeddings-for-cold-start","slug":"learning-graph-meta-embeddings-for-cold-start","title":"Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction","date":"2021-05-19","arxiv_id":"2105.08909","repositories_listed":1,"syntology":null},{"url":"/paper/looking-at-ctr-prediction-again-is-attention","slug":"looking-at-ctr-prediction-again-is-attention","title":"Looking at CTR Prediction Again: Is Attention All You Need?","date":"2021-05-12","arxiv_id":"2105.05563","repositories_listed":1,"syntology":null},{"url":"/paper/xcrossnet-feature-structure-oriented-learning","slug":"xcrossnet-feature-structure-oriented-learning","title":"XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction","date":"2021-04-22","arxiv_id":"2104.10907","repositories_listed":1,"syntology":null},{"url":"/paper/generating-multi-type-sequences-of-temporal","slug":"generating-multi-type-sequences-of-temporal","title":"Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem","date":"2021-04-07","arxiv_id":"2104.03428","repositories_listed":1,"syntology":null},{"url":"/paper/automated-creative-optimization-for-e","slug":"automated-creative-optimization-for-e","title":"Automated Creative Optimization for E-Commerce Advertising","date":"2021-02-28","arxiv_id":"2103.00436","repositories_listed":1,"syntology":null},{"url":"/paper/exploration-in-online-advertising-systems","slug":"exploration-in-online-advertising-systems","title":"Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning","date":"2020-11-25","arxiv_id":"2012.02298","repositories_listed":1,"syntology":null},{"url":"/paper/learning-interaction-models-of-structured","slug":"learning-interaction-models-of-structured","title":"GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network","date":"2020-11-25","arxiv_id":"2011.12683","repositories_listed":1,"syntology":null},{"url":"/paper/deep-multi-interest-network-for-click-through","slug":"deep-multi-interest-network-for-click-through","title":"Deep Multi-Interest Network for Click-through Rate Prediction","date":"2020-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/click-is-not-equal-to-like-counterfactual","slug":"click-is-not-equal-to-like-counterfactual","title":"Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue","date":"2020-09-21","arxiv_id":"2009.09945","repositories_listed":1,"syntology":null},{"url":"/paper/minet-mixed-interest-network-for-cross-domain","slug":"minet-mixed-interest-network-for-cross-domain","title":"MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction","date":"2020-08-07","arxiv_id":"2008.02974","repositories_listed":1,"syntology":null},{"url":"/paper/fedctr-federated-native-ad-ctr-prediction","slug":"fedctr-federated-native-ad-ctr-prediction","title":"FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data","date":"2020-07-23","arxiv_id":"2007.12135","repositories_listed":1,"syntology":null},{"url":"/paper/autorec-an-automated-recommender-system","slug":"autorec-an-automated-recommender-system","title":"AutoRec: An Automated Recommender System","date":"2020-06-26","arxiv_id":"2007.07224","repositories_listed":1,"syntology":null},{"url":"/paper/correct-normalization-matters-understanding","slug":"correct-normalization-matters-understanding","title":"Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction","date":"2020-06-23","arxiv_id":"2006.12753","repositories_listed":1,"syntology":null},{"url":"/paper/user-behavior-retrieval-for-click-through","slug":"user-behavior-retrieval-for-click-through","title":"User Behavior Retrieval for Click-Through Rate Prediction","date":"2020-05-28","arxiv_id":"2005.14171","repositories_listed":1,"syntology":null},{"url":"/paper/deep-interest-with-hierarchical-attention","slug":"deep-interest-with-hierarchical-attention","title":"Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction","date":"2020-05-22","arxiv_id":"2005.12981","repositories_listed":1,"syntology":null},{"url":"/paper/deep-interaction-machine-a-simple-but-1","slug":"deep-interaction-machine-a-simple-but-1","title":"Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-end-to-end-neighborhood-based-interaction","slug":"an-end-to-end-neighborhood-based-interaction","title":"An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation","date":"2019-08-12","arxiv_id":"1908.04032","repositories_listed":1,"syntology":null},{"url":"/paper/click-through-rate-prediction-with-the-user","slug":"click-through-rate-prediction-with-the-user","title":"Click-Through Rate Prediction with the User Memory Network","date":"2019-07-09","arxiv_id":"1907.04667","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-assisted-click","slug":"representation-learning-assisted-click","title":"Representation Learning-Assisted Click-Through Rate Prediction","date":"2019-06-11","arxiv_id":"1906.04365","repositories_listed":1,"syntology":null},{"url":"/paper/deep-spatio-temporal-neural-networks-for","slug":"deep-spatio-temporal-neural-networks-for","title":"Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction","date":"2019-06-10","arxiv_id":"1906.03776","repositories_listed":1,"syntology":null},{"url":"/paper/warm-up-cold-start-advertisements-improving","slug":"warm-up-cold-start-advertisements-improving","title":"Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings","date":"2019-04-25","arxiv_id":"1904.11547","repositories_listed":1,"syntology":null},{"url":"/paper/deep-character-level-click-through-rate","slug":"deep-character-level-click-through-rate","title":"Deep Character-Level Click-Through Rate Prediction for Sponsored Search","date":"2017-07-07","arxiv_id":"1707.02158","repositories_listed":1,"syntology":null},{"url":null,"slug":"generative-click-through-rate-prediction-with","title":"Generative Click-through Rate Prediction with Applications to Search Advertising","date":"2025-07-15","arxiv_id":"2507.11246","repositories_listed":0,"syntology":null},{"url":null,"slug":"gist-cross-domain-click-through-rate","title":"GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation","date":"2025-07-07","arxiv_id":"2507.05142","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-audio-centric-multi-task-learning","title":"An Audio-centric Multi-task Learning Framework for Streaming Ads Targeting on Spotify","date":"2025-06-23","arxiv_id":"2506.18735","repositories_listed":0,"syntology":null},{"url":null,"slug":"field-matters-a-lightweight-llm-enhanced","title":"Field Matters: A lightweight LLM-enhanced Method for CTR Prediction","date":"2025-05-20","arxiv_id":"2505.14057","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-practice-of-deep-hierarchical-ensemble","title":"On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction","date":"2025-04-10","arxiv_id":"2504.08169","repositories_listed":0,"syntology":null},{"url":null,"slug":"prectr-a-synergistic-framework-for","title":"PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction","date":"2025-03-24","arxiv_id":"2503.18395","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-cross-domain-click-through-rate","title":"Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation","date":"2025-03-21","arxiv_id":"2503.16875","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-information-loss-and-interaction","title":"Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction","date":"2025-03-14","arxiv_id":"2503.11233","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-llms-in-recommendation-tasks-a","title":"Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders","date":"2025-03-07","arxiv_id":"2503.05493","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-long-sequential-low-rank-adaptive","title":"LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction","date":"2025-03-04","arxiv_id":"2503.02542","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-an-efficient-llm-training-paradigm","title":"Towards An Efficient LLM Training Paradigm for CTR Prediction","date":"2025-03-02","arxiv_id":"2503.01001","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-cold-start-problem-in-click","title":"Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling","date":"2025-03-01","arxiv_id":"2504.06270","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-context-information-in-lifelong","title":"Context-Aware Lifelong Sequential Modeling for Online Click-Through Rate Prediction","date":"2025-02-18","arxiv_id":"2502.12634","repositories_listed":0,"syntology":null},{"url":null,"slug":"mim-multi-modal-content-interest-modeling","title":"MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling","date":"2025-02-01","arxiv_id":"2502.00321","repositories_listed":0,"syntology":null},{"url":null,"slug":"general-information-metrics-for-improving-ai","title":"General Information Metrics for Improving AI Model Training Efficiency","date":"2025-01-02","arxiv_id":"2501.02004","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-efficiency-and-effectiveness-an-llm","title":"Balancing Efficiency and Effectiveness: An LLM-Infused Approach for Optimized CTR Prediction","date":"2024-12-09","arxiv_id":"2412.06860","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ctr-prediction-via-llm-reasoning","title":"Explainable CTR Prediction via LLM Reasoning","date":"2024-12-03","arxiv_id":"2412.02588","repositories_listed":0,"syntology":null},{"url":null,"slug":"liber-lifelong-user-behavior-modeling-based","title":"LIBER: Lifelong User Behavior Modeling Based on Large Language Models","date":"2024-11-22","arxiv_id":"2411.14713","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-accuracy-improving-method-for-advertising","title":"An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model","date":"2024-11-21","arxiv_id":"2411.15223","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-collaborative-ensemble-framework-for-ctr","title":"A Collaborative Ensemble Framework for CTR Prediction","date":"2024-11-20","arxiv_id":"2411.13700","repositories_listed":0,"syntology":null},{"url":null,"slug":"branches-assemble-multi-branch-cooperation","title":"Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale Click-Through Rate Prediction at Taobao","date":"2024-11-20","arxiv_id":"2411.13057","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unifying-feature-interaction-models","title":"Towards Unifying Feature Interaction Models for Click-Through Rate Prediction","date":"2024-11-19","arxiv_id":"2411.12441","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-domain-moveline-evolution-network-for","title":"All-domain Moveline Evolution Network for Click-Through Rate Prediction","date":"2024-11-18","arxiv_id":"2411.11502","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-contrastive-network-for-click","title":"Collaborative Contrastive Network for Click-Through Rate Prediction","date":"2024-11-18","arxiv_id":"2411.11508","repositories_listed":0,"syntology":null},{"url":null,"slug":"interformer-towards-effective-heterogeneous","title":"InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction","date":"2024-11-15","arxiv_id":"2411.09852","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-interaction-fusion-self-distillation","title":"Feature Interaction Fusion Self-Distillation Network For CTR Prediction","date":"2024-11-12","arxiv_id":"2411.07508","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-cross-correlated-network-for","title":"Graph Cross-Correlated Network for Recommendation","date":"2024-11-02","arxiv_id":"2411.01182","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-ctr-prediction-in-recommendation","title":"Enhancing CTR Prediction in Recommendation Domain with Search Query Representation","date":"2024-10-28","arxiv_id":"2410.21487","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-recommendation-model-utilizing-separation","title":"A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining","date":"2024-10-19","arxiv_id":"2410.15026","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-group-prior-into-variational","title":"Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction","date":"2024-10-19","arxiv_id":"2410.15098","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficiency-for-large-recommendation","title":"Data Efficiency for Large Recommendation Models","date":"2024-10-08","arxiv_id":"2410.18111","repositories_listed":0,"syntology":null},{"url":null,"slug":"neshfs-neighborhood-search-with-heuristic","title":"NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction","date":"2024-09-13","arxiv_id":"2409.08703","repositories_listed":0,"syntology":null},{"url":null,"slug":"rboard-a-unified-platform-for-reproducible","title":"RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks","date":"2024-09-09","arxiv_id":"2409.05526","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-transfer-learning-framework-for","title":"Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction","date":"2024-08-29","arxiv_id":"2408.16238","repositories_listed":0,"syntology":null},{"url":null,"slug":"larr-large-language-model-aided-real-time","title":"LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding","date":"2024-08-21","arxiv_id":"2408.11523","repositories_listed":0,"syntology":null}],"record_sha256":"b8ca3e9f33f2577554dadb9fd66d783c2152a4c88107dabb1f1089f6a9cc5e9d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}