{"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/recommendation-systems/papers/38","list_of":"/task/recommendation-systems","task":"Recommendation Systems","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":38,"pages_in_order":61,"rows_per_page":100,"rows":[3701,3800],"of":6047,"counts":{"archive_papers_tagged":6047,"with_a_code_link":1997,"where_syntology_ran_a_sample":330,"not_listed_spam_title":0,"listed":6047,"listed_where_code_ran":330,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":280,"every_run_a_failure_of_syntologys_instrument":50,"listed_with_a_run_with_no_instrument_failure":280,"listed_every_run_a_failure_of_syntologys_instrument":50,"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/recommendation-systems","prev":"/task/recommendation-systems/papers/37","next":"/task/recommendation-systems/papers/39","papers":[{"url":null,"slug":"dor-a-novel-dual-observation-based-approach","title":"DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems","date":"2023-02-02","arxiv_id":"2302.01443","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-exposure-constraints-in","title":"Learning with Exposure Constraints in Recommendation Systems","date":"2023-02-02","arxiv_id":"2302.01377","repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-decision-transformer-intended","title":"Clinical Decision Transformer: Intended Treatment Recommendation through Goal Prompting","date":"2023-02-01","arxiv_id":"2302.00612","repositories_listed":0,"syntology":null},{"url":null,"slug":"delayed-feedback-in-kernel-bandits","title":"Delayed Feedback in Kernel Bandits","date":"2023-02-01","arxiv_id":"2302.00392","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-cross-domain-recommendation","title":"Fairness-aware Cross-Domain Recommendation","date":"2023-02-01","arxiv_id":"2302.00158","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ci-through-collective-response","title":"'Generative CI' through Collective Response Systems","date":"2023-02-01","arxiv_id":"2302.00672","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-visualization","title":"Agnostic Visual Recommendation Systems: Open Challenges and Future Directions","date":"2023-02-01","arxiv_id":"2302.00569","repositories_listed":0,"syntology":null},{"url":null,"slug":"complete-neural-networks-for-euclidean-graphs","title":"Complete Neural Networks for Complete Euclidean Graphs","date":"2023-01-31","arxiv_id":"2301.13821","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-contextual-bandits-and-recommender","title":"Quantum contextual bandits and recommender systems for quantum data","date":"2023-01-31","arxiv_id":"2301.13524","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-on-adversarial-bandits","title":"Adversarial Attacks on Adversarial Bandits","date":"2023-01-30","arxiv_id":"2301.12595","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommender-system-as-an-exploration","title":"Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle","date":"2023-01-29","arxiv_id":"2301.12571","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-multi-behavior-sequence-modeling-for","title":"Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation","date":"2023-01-28","arxiv_id":"2301.12105","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-synthetic-data-for-conversational","title":"Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation","date":"2023-01-27","arxiv_id":"2301.11489","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-recommendation-via-user-interest","title":"Cross-domain recommendation via user interest alignment","date":"2023-01-26","arxiv_id":"2301.11467","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolution-of-filter-bubbles-and-polarization","title":"Evolution of Filter Bubbles and Polarization in News Recommendation","date":"2023-01-26","arxiv_id":"2301.10926","repositories_listed":0,"syntology":null},{"url":null,"slug":"interaction-level-membership-inference-attack","title":"Interaction-level Membership Inference Attack Against Federated Recommender Systems","date":"2023-01-26","arxiv_id":"2301.10964","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-l-a-book-recommender-system","title":"BRAIN L: A book recommender system","date":"2023-01-25","arxiv_id":"2302.00653","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-rank-normalized-entropy-curves","title":"Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation","date":"2023-01-25","arxiv_id":"2301.10443","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-graph-neural-networks-a","title":"Spatio-Temporal Graph Neural Networks: A Survey","date":"2023-01-25","arxiv_id":"2301.10569","repositories_listed":0,"syntology":null},{"url":null,"slug":"transferable-fairness-for-cold-start","title":"Transferable Fairness for Cold-Start Recommendation","date":"2023-01-25","arxiv_id":"2301.10665","repositories_listed":0,"syntology":null},{"url":null,"slug":"gbose-generalized-bandit-orthogonalized","title":"GBOSE: Generalized Bandit Orthogonalized Semiparametric Estimation","date":"2023-01-20","arxiv_id":"2301.08781","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-slate-recommendation-with","title":"Generative Slate Recommendation with Reinforcement Learning","date":"2023-01-20","arxiv_id":"2301.08632","repositories_listed":0,"syntology":null},{"url":null,"slug":"everything-is-connected-graph-neural-networks","title":"Everything is Connected: Graph Neural Networks","date":"2023-01-19","arxiv_id":"2301.08210","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-bias-amplification","title":"A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems","date":"2023-01-18","arxiv_id":"2301.07639","repositories_listed":0,"syntology":null},{"url":null,"slug":"biases-in-scholarly-recommender-systems","title":"Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation","date":"2023-01-18","arxiv_id":"2301.07483","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-filtering-over-expanding-graphs","title":"Online Filtering over Expanding Graphs","date":"2023-01-17","arxiv_id":"2301.06898","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-algorithms-for-latent-bandits-with","title":"Optimal Algorithms for Latent Bandits with Cluster Structure","date":"2023-01-17","arxiv_id":"2301.07040","repositories_listed":0,"syntology":null},{"url":null,"slug":"reusable-self-attention-recommender-systems","title":"Reusable Self-Attention Recommender Systems in Fashion Industry Applications","date":"2023-01-17","arxiv_id":"2301.06777","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-design-of-user-centric-strategy","title":"Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks","date":"2023-01-17","arxiv_id":"2301.08144","repositories_listed":0,"syntology":null},{"url":null,"slug":"ae-2i-a-double-autoencoder-for-imputation-of","title":"$Ae^2I$: A Double Autoencoder for Imputation of Missing Values","date":"2023-01-16","arxiv_id":"2301.06633","repositories_listed":0,"syntology":null},{"url":null,"slug":"failure-tolerant-training-with-persistent","title":"Failure Tolerant Training with Persistent Memory Disaggregation over CXL","date":"2023-01-14","arxiv_id":"2301.07492","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-enhancement-for-multi-behavior","title":"Knowledge Enhancement for Contrastive Multi-Behavior Recommendation","date":"2023-01-13","arxiv_id":"2301.05403","repositories_listed":0,"syntology":null},{"url":null,"slug":"against-algorithmic-exploitation-of-human","title":"Against Algorithmic Exploitation of Human Vulnerabilities","date":"2023-01-12","arxiv_id":"2301.04993","repositories_listed":0,"syntology":null},{"url":null,"slug":"much-ado-about-gender-current-practices-and","title":"Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access","date":"2023-01-12","arxiv_id":"2301.04780","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-recommendation-by-geometric","title":"Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization","date":"2023-01-10","arxiv_id":"2301.03791","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-inference-for-recommendation","title":"Causal Inference for Recommendation: Foundations, Methods and Applications","date":"2023-01-08","arxiv_id":"2301.04016","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-human-and-computer-opinion-fraud","title":"Mitigating Human and Computer Opinion Fraud via Contrastive Learning","date":"2023-01-08","arxiv_id":"2301.03025","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaensemble-learning-adaptively-sparse","title":"AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction","date":"2023-01-06","arxiv_id":"2301.08353","repositories_listed":0,"syntology":null},{"url":null,"slug":"max-min-diversification-with-fairness","title":"Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms","date":"2023-01-05","arxiv_id":"2301.02053","repositories_listed":0,"syntology":null},{"url":null,"slug":"episodes-discovery-recommendation-with-multi","title":"Episodes Discovery Recommendation with Multi-Source Augmentations","date":"2023-01-04","arxiv_id":"2301.01737","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynint-dynamic-interaction-modeling-for-large","title":"DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction","date":"2023-01-03","arxiv_id":"2301.08139","repositories_listed":0,"syntology":null},{"url":null,"slug":"multidimensional-item-response-theory-in-the","title":"Multidimensional Item Response Theory in the Style of Collaborative Filtering","date":"2023-01-03","arxiv_id":"2301.00909","repositories_listed":0,"syntology":null},{"url":null,"slug":"offline-evaluation-for-reinforcement-learning","title":"Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives","date":"2023-01-03","arxiv_id":"2301.00993","repositories_listed":0,"syntology":null},{"url":null,"slug":"ontology-based-context-aware-recommender","title":"Ontology-based Context Aware Recommender System Application for Tourism","date":"2022-12-29","arxiv_id":"2301.00768","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-federated-recommendation-systems","title":"A Survey on Federated Recommendation Systems","date":"2022-12-27","arxiv_id":"2301.00767","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-time-series-and-spatial-data-for","title":"Modeling Time-Series and Spatial Data for Recommendations and Other Applications","date":"2022-12-25","arxiv_id":"2212.13259","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommending-on-graphs-a-comprehensive-review","title":"Recommending on graphs: a comprehensive review from a data perspective","date":"2022-12-23","arxiv_id":"2212.12230","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-you-like-generating-explainable-topical","title":"What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations","date":"2022-12-23","arxiv_id":"2212.13897","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-dataset-propensity-estimation-for","title":"Cross-Dataset Propensity Estimation for Debiasing Recommender Systems","date":"2022-12-22","arxiv_id":"2212.13892","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-policy-improvement-for-recommender","title":"Local Policy Improvement for Recommender Systems","date":"2022-12-22","arxiv_id":"2212.11431","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-ties-that-matter-from-the-perspective-of","title":"The Ties that matter: From the perspective of Similarity Measure in Online Social Networks","date":"2022-12-21","arxiv_id":"2212.10960","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-metric-autorec-for-high-dimensional-and","title":"Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction","date":"2022-12-20","arxiv_id":"2212.13879","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-linear-mixed-model-for-group","title":"Learning with linear mixed model for group recommendation systems","date":"2022-12-17","arxiv_id":"2212.08901","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-federated-recommender-systems","title":"Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders","date":"2022-12-17","arxiv_id":"2212.08779","repositories_listed":0,"syntology":null},{"url":null,"slug":"cola-improving-conversational-recommender","title":"COLA: Improving Conversational Recommender Systems by Collaborative Augmentation","date":"2022-12-15","arxiv_id":"2212.07767","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifestations-of-xenophobia-in-ai-systems","title":"Manifestations of Xenophobia in AI Systems","date":"2022-12-15","arxiv_id":"2212.07877","repositories_listed":0,"syntology":null},{"url":null,"slug":"membership-inference-attacks-against-latent","title":"Membership Inference Attacks Against Latent Factor Model","date":"2022-12-15","arxiv_id":"2301.03596","repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-maximum-inner-product-search-in-high","title":"Faster Maximum Inner Product Search in High Dimensions","date":"2022-12-14","arxiv_id":"2212.07551","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-researchers-could-obtain-quick-and-cheap","title":"How Researchers Could Obtain Quick and Cheap User Feedback on their Algorithms Without Having to Operate their Own Recommender System","date":"2022-12-14","arxiv_id":"2212.07177","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairroad-achieving-fairness-for-recommender","title":"FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data","date":"2022-12-13","arxiv_id":"2212.06750","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-learning-with-pricing-for-optimal","title":"Interactive Learning with Pricing for Optimal and Stable Allocations in Markets","date":"2022-12-13","arxiv_id":"2212.06891","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommender-systems-in-e-commerce","title":"Recommender Systems in E-commerce","date":"2022-12-13","arxiv_id":"2212.13910","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-leakage-via-access-patterns-of-sparse","title":"Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems","date":"2022-12-12","arxiv_id":"2212.06264","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-synthetic-datasets-for","title":"Evaluation of Synthetic Datasets for Conversational Recommender Systems","date":"2022-12-12","arxiv_id":"2212.08167","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-off-policy-learning-from-bandit","title":"Multi-Task Off-Policy Learning from Bandit Feedback","date":"2022-12-09","arxiv_id":"2212.04720","repositories_listed":0,"syntology":null},{"url":null,"slug":"tinykg-memory-efficient-training-framework","title":"TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems","date":"2022-12-08","arxiv_id":"2212.04540","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-drug-repurposing-knowledge-graphs","title":"Analysis of Drug repurposing Knowledge graphs for Covid-19","date":"2022-12-07","arxiv_id":"2212.03911","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-and-analyzing-the-resilience-of","title":"PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors","date":"2022-12-07","arxiv_id":"2212.03475","repositories_listed":0,"syntology":null},{"url":null,"slug":"pivotal-role-of-language-modeling-in","title":"Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning","date":"2022-12-07","arxiv_id":"2212.03760","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-recognition-in-conversational","title":"Intent Recognition in Conversational Recommender Systems","date":"2022-12-06","arxiv_id":"2212.03721","repositories_listed":0,"syntology":null},{"url":null,"slug":"pareto-pairwise-ranking-for-fairness","title":"Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems","date":"2022-12-06","arxiv_id":"2212.10459","repositories_listed":0,"syntology":null},{"url":null,"slug":"poissonmat-remodeling-matrix-factorization","title":"PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data","date":"2022-12-06","arxiv_id":"2212.10460","repositories_listed":0,"syntology":null},{"url":null,"slug":"matrix-factorization-with-neural-networks","title":"Matrix factorization with neural networks","date":"2022-12-05","arxiv_id":"2212.02105","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-and-eliciting-needs-and-preferences","title":"Exploring and Eliciting Needs and Preferences from Editors for Wikidata Recommendations","date":"2022-12-04","arxiv_id":"2212.01818","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-shop-improving-item-advertisement-for","title":"Meta-Shop: Improving Item Advertisement For Small Businesses","date":"2022-12-02","arxiv_id":"2212.01414","repositories_listed":0,"syntology":null},{"url":null,"slug":"movie-recommendation-system-using-composite","title":"Movie Recommendation System using Composite Ranking","date":"2022-11-30","arxiv_id":"2212.00139","repositories_listed":0,"syntology":null},{"url":null,"slug":"reusable-self-attention-based-recommender","title":"Reusable Self-Attention-based Recommender System for Fashion","date":"2022-11-29","arxiv_id":"2211.16366","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-conversational-search-and","title":"A Survey on Conversational Search and Applications in Biomedicine","date":"2022-11-28","arxiv_id":"2211.15328","repositories_listed":0,"syntology":null},{"url":null,"slug":"adatask-a-task-aware-adaptive-learning-rate","title":"AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning","date":"2022-11-28","arxiv_id":"2211.15055","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-learning-as-a-service-with-covariance","title":"Metric Learning as a Service with Covariance Embedding","date":"2022-11-28","arxiv_id":"2211.15197","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-reliable-item-sampling-for","title":"Towards Reliable Item Sampling for Recommendation Evaluation","date":"2022-11-28","arxiv_id":"2211.15743","repositories_listed":0,"syntology":null},{"url":null,"slug":"recxplainer-post-hoc-attribute-based","title":"RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems","date":"2022-11-27","arxiv_id":"2211.14935","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-text-representation-methods-and","title":"A Survey of Text Representation Methods and Their Genealogy","date":"2022-11-26","arxiv_id":"2211.14591","repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-bpr-loss-for-dynamic-hard-negative","title":"Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact","date":"2022-11-25","arxiv_id":"2211.13912","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-economics-of-recommender-systems-evidence","title":"The Informational Role of Online Recommendations: Evidence from a Field Experiment","date":"2022-11-25","arxiv_id":"2211.14219","repositories_listed":0,"syntology":null},{"url":null,"slug":"prototypical-contrastive-learning-and","title":"Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations","date":"2022-11-23","arxiv_id":"2211.12893","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptdhm-adaptive-distribution-hierarchical","title":"AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction","date":"2022-11-22","arxiv_id":"2211.12105","repositories_listed":0,"syntology":null},{"url":null,"slug":"basm-a-bottom-up-adaptive-spatiotemporal","title":"BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service","date":"2022-11-22","arxiv_id":"2211.12033","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlative-preference-transfer-with","title":"Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation","date":"2022-11-21","arxiv_id":"2211.11191","repositories_listed":0,"syntology":null},{"url":null,"slug":"influential-recommender-system","title":"Influential Recommender System","date":"2022-11-18","arxiv_id":"2211.10002","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-user-intent-modeling-for-sequential","title":"Latent User Intent Modeling for Sequential Recommenders","date":"2022-11-17","arxiv_id":"2211.09832","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-counterfactually-explain","title":"Learning to Counterfactually Explain Recommendations","date":"2022-11-17","arxiv_id":"2211.09752","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-frequency-bias-in-next-basket","title":"Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders","date":"2022-11-16","arxiv_id":"2211.09072","repositories_listed":0,"syntology":null},{"url":null,"slug":"speeding-up-recommender-systems-using","title":"Speeding Up Recommender Systems Using Association Rules","date":"2022-11-16","arxiv_id":"2211.08799","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-accuracy-and-autonomy","title":"Prediction Accuracy and Autonomy","date":"2022-11-15","arxiv_id":"2211.08134","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-specific-bicluster-based-collaborative","title":"User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity","date":"2022-11-15","arxiv_id":"2211.08366","repositories_listed":0,"syntology":null},{"url":null,"slug":"significant-ties-graph-neural-networks-for","title":"Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling","date":"2022-11-12","arxiv_id":"2211.06590","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-aware-multi-source-contrastive","title":"Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation","date":"2022-11-11","arxiv_id":"2211.06370","repositories_listed":0,"syntology":null},{"url":null,"slug":"situating-recommender-systems-in-practice","title":"Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates","date":"2022-11-11","arxiv_id":"2211.06365","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-fraudster-detection-contributes-to-robust","title":"Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection","date":"2022-11-08","arxiv_id":"2211.11534","repositories_listed":0,"syntology":null}],"record_sha256":"227299fcf908cedf2f04ec2299f97fd29995712adeb1793c2b64706c956210c2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}