{"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/39","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":39,"pages_in_order":61,"rows_per_page":100,"rows":[3801,3900],"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/38","next":"/task/recommendation-systems/papers/40","papers":[{"url":null,"slug":"hyperbolic-graph-representation-learning-a","title":"Hyperbolic Graph Representation Learning: A Tutorial","date":"2022-11-08","arxiv_id":"2211.04050","repositories_listed":0,"syntology":null},{"url":null,"slug":"submission-aware-reviewer-profiling-for","title":"Submission-Aware Reviewer Profiling for Reviewer Recommender System","date":"2022-11-08","arxiv_id":"2211.04194","repositories_listed":0,"syntology":null},{"url":null,"slug":"timekit-a-time-series-forecasting-based","title":"TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering","date":"2022-11-08","arxiv_id":"2211.04266","repositories_listed":0,"syntology":null},{"url":null,"slug":"justification-of-recommender-systems-results","title":"Justification of Recommender Systems Results: A Service-based Approach","date":"2022-11-07","arxiv_id":"2211.03452","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-influence-maximization-from-an-ml","title":"A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization","date":"2022-11-06","arxiv_id":"2211.03074","repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-recommendation-and-conversation-via","title":"Aligning Recommendation and Conversation via Dual Imitation","date":"2022-11-05","arxiv_id":"2211.02848","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-factorization-model-for-robust","title":"Deep Factorization Model for Robust Recommendation","date":"2022-11-05","arxiv_id":"2211.02894","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-user-interests-through-topic-tag","title":"Forecasting User Interests Through Topic Tag Predictions in Online Health Communities","date":"2022-11-05","arxiv_id":"2211.02789","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-do-we-go-from-here-guidelines-for","title":"Where Do We Go From Here? Guidelines For Offline Recommender Evaluation","date":"2022-11-02","arxiv_id":"2211.01261","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-hashing-for-fast-ranking-via","title":"Asymmetric Hashing for Fast Ranking via Neural Network Measures","date":"2022-11-01","arxiv_id":"2211.00619","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategies-for-optimizing-end-to-end","title":"Strategies for Optimizing End-to-End Artificial Intelligence Pipelines on Intel Xeon Processors","date":"2022-11-01","arxiv_id":"2211.00286","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-coevolution-and-substitution-of-the","title":"Using coevolution and substitution of the fittest for health and well-being recommender systems","date":"2022-11-01","arxiv_id":"2211.00414","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-machine-learning-and-pattern-based","title":"Using Machine Learning and Pattern-Based Methods for Identifying Elements in Chinese Judgment Documents of Civil Cases","date":"2022-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-popularity-bias-in-recommendation","title":"Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective","date":"2022-10-31","arxiv_id":"2211.01154","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovery-dynamics-leveraging-repeated","title":"Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization","date":"2022-10-28","arxiv_id":"2210.16226","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-bandits-with-an-auto-regressive","title":"Non-Stationary Bandits with Auto-Regressive Temporal Dependency","date":"2022-10-28","arxiv_id":"2210.16386","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-approximate-similarity-search-on","title":"Constrained Approximate Similarity Search on Proximity Graph","date":"2022-10-26","arxiv_id":"2210.14958","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-long-tail-item-recommendation","title":"Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)","date":"2022-10-25","arxiv_id":"2210.14309","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedgrec-federated-graph-recommender-system","title":"FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings","date":"2022-10-25","arxiv_id":"2210.13686","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommendation-with-user-active-disclosing","title":"Recommendation with User Active Disclosing Willingness","date":"2022-10-25","arxiv_id":"2211.01155","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-recommender-systems-via-triple","title":"Towards Robust Recommender Systems via Triple Cooperative Defense","date":"2022-10-25","arxiv_id":"2210.13762","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-information-crossing-on-graphs","title":"Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems","date":"2022-10-24","arxiv_id":"2210.12940","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-information-retrieval-evaluation-using","title":"Online Information Retrieval Evaluation using the STELLA Framework","date":"2022-10-24","arxiv_id":"2210.13202","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-and-bandits-for-speech","title":"Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook","date":"2022-10-24","arxiv_id":"2210.13623","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-model-fine-tuning-with-label","title":"On-Device Model Fine-Tuning with Label Correction in Recommender Systems","date":"2022-10-21","arxiv_id":"2211.01163","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-employing-recommender-systems-for","title":"Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing","date":"2022-10-21","arxiv_id":"2210.11828","repositories_listed":0,"syntology":null},{"url":null,"slug":"entire-space-counterfactual-learning-tuning","title":"Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications","date":"2022-10-20","arxiv_id":"2210.11039","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-unlearning-for-on-device","title":"Federated Unlearning for On-Device Recommendation","date":"2022-10-20","arxiv_id":"2210.10958","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-representations-towards","title":"Low-Rank Representations Towards Classification Problem of Complex Networks","date":"2022-10-20","arxiv_id":"2210.11561","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-structure-learning-with-recommendation","title":"Causal Structure Learning with Recommendation System","date":"2022-10-19","arxiv_id":"2210.10256","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-recommender-systems-survey","title":"Multi-Objective Recommender Systems: Survey and Challenges","date":"2022-10-19","arxiv_id":"2210.10309","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-extreme-cold-start-problem-in","title":"Addressing the Extreme Cold-Start Problem in Group Recommendation","date":"2022-10-18","arxiv_id":"2210.09672","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-search-explainability-with","title":"Evaluating Search System Explainability with Psychometrics and Crowdsourcing","date":"2022-10-17","arxiv_id":"2210.09430","repositories_listed":0,"syntology":null},{"url":null,"slug":"merlin-hugectr-gpu-accelerated-recommender","title":"Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference","date":"2022-10-17","arxiv_id":"2210.08803","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-undergraduate-data-collection","title":"A Framework for Undergraduate Data Collection Strategies for Student Support Recommendation Systems in Higher Education","date":"2022-10-16","arxiv_id":"2210.10657","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-the-smallest-or-largest-element-of-a","title":"Minimizing low-rank models of high-order tensors: Hardness, span, tight relaxation, and applications","date":"2022-10-16","arxiv_id":"2210.11413","repositories_listed":0,"syntology":null},{"url":null,"slug":"off-policy-evaluation-for-learning-to-rank","title":"Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model","date":"2022-10-15","arxiv_id":"2210.09512","repositories_listed":0,"syntology":null},{"url":null,"slug":"mv-han-a-hybrid-attentive-networks-based","title":"MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation","date":"2022-10-14","arxiv_id":"2210.07660","repositories_listed":0,"syntology":null},{"url":null,"slug":"shadfa-0-1-the-iranian-movie-knowledge-graph","title":"Shadfa 0.1: The Iranian Movie Knowledge Graph and Graph-Embedding-Based Recommender System","date":"2022-10-14","arxiv_id":"2210.07822","repositories_listed":0,"syntology":null},{"url":null,"slug":"simpson-s-paradox-in-recommender-fairness","title":"Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations","date":"2022-10-14","arxiv_id":"2210.07755","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-recommendation-system-with","title":"Multi-Modal Recommendation System with Auxiliary Information","date":"2022-10-13","arxiv_id":"2210.10652","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-or-manipulation-rethinking","title":"Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems","date":"2022-10-11","arxiv_id":"2210.05662","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spectral-approach-to-item-response-theory","title":"A Spectral Approach to Item Response Theory","date":"2022-10-09","arxiv_id":"2210.04317","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-counter-intuitive-observations-to-a","title":"Take a Fresh Look at Recommender Systems from an Evaluation Standpoint","date":"2022-10-09","arxiv_id":"2210.04149","repositories_listed":0,"syntology":null},{"url":null,"slug":"kast-knowledge-aware-adaptive-session-multi","title":"KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction","date":"2022-10-07","arxiv_id":"2210.03624","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-and-efficient-meta-learning-with-low","title":"Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components","date":"2022-10-07","arxiv_id":"2210.03505","repositories_listed":0,"syntology":null},{"url":null,"slug":"scientific-and-technological-news","title":"Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception","date":"2022-10-07","arxiv_id":"2210.03295","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-interventions-to-improve-out-of","title":"Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems","date":"2022-10-07","arxiv_id":"2210.10636","repositories_listed":0,"syntology":null},{"url":null,"slug":"igniter-news-recommendation-in-microblogging","title":"IGNiteR: News Recommendation in Microblogging Applications (Extended Version)","date":"2022-10-04","arxiv_id":"2210.01942","repositories_listed":0,"syntology":null},{"url":null,"slug":"accuracy-meets-diversity-in-a-news","title":"Accuracy meets Diversity in a News Recommender System","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cr-gis-improving-conversational","title":"CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling","date":"2022-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sequence-aware-recommendation-method-based","title":"A Sequence-Aware Recommendation Method Based on Complex Networks","date":"2022-09-30","arxiv_id":"2210.07814","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-graph-based-recommender-system-with","title":"Efficient Graph based Recommender System with Weighted Averaging of Messages","date":"2022-09-30","arxiv_id":"2209.15238","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-recommendation-approach-based-on-similarity","title":"A Recommendation Approach based on Similarity-Popularity Models of Complex Networks","date":"2022-09-29","arxiv_id":"2210.07816","repositories_listed":0,"syntology":null},{"url":null,"slug":"privmvmf-privacy-preserving-multi-view-matrix","title":"PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems","date":"2022-09-29","arxiv_id":"2210.07775","repositories_listed":0,"syntology":null},{"url":null,"slug":"discussion-about-attacks-and-defenses-for","title":"Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design","date":"2022-09-28","arxiv_id":"2210.07817","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-information-and-ensemble-based-feature","title":"Mutual Information Assisted Ensemble Recommender System for Identifying Critical Risk Factors in Healthcare Prognosis","date":"2022-09-28","arxiv_id":"2209.13836","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-graph-neural-networks-and-graph","title":"A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective","date":"2022-09-27","arxiv_id":"2209.13232","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-asymptotically-optimal-batched-algorithm","title":"An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem","date":"2022-09-25","arxiv_id":"2209.12108","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpatch-patching-graph-neural-networks-for","title":"GPatch: Patching Graph Neural Networks for Cold-Start Recommendations","date":"2022-09-25","arxiv_id":"2209.12215","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-conversational-recommender-system","title":"Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge","date":"2022-09-23","arxiv_id":"2209.11386","repositories_listed":0,"syntology":null},{"url":null,"slug":"m2trec-metadata-aware-multi-task-transformer","title":"M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations","date":"2022-09-23","arxiv_id":"2209.11824","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-augmented-graph-neural-networks-a","title":"Memory-Augmented Graph Neural Networks: A Brain-Inspired Review","date":"2022-09-22","arxiv_id":"2209.10818","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-trustworthy","title":"A Comprehensive Survey on Trustworthy Recommender Systems","date":"2022-09-21","arxiv_id":"2209.10117","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-anomaly-detection","title":"Collaborative Anomaly Detection","date":"2022-09-20","arxiv_id":"2209.09923","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-understanding-privileged-features","title":"Toward Understanding Privileged Features Distillation in Learning-to-Rank","date":"2022-09-19","arxiv_id":"2209.08754","repositories_listed":0,"syntology":null},{"url":null,"slug":"offline-evaluation-of-reward-optimizing","title":"Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation","date":"2022-09-18","arxiv_id":"2209.08642","repositories_listed":0,"syntology":null},{"url":null,"slug":"beware-of-the-ostrich-policy-end-users","title":"Beware of the Ostrich Policy: End-Users' Perceptions Towards Data Transparency and Control","date":"2022-09-17","arxiv_id":"2209.08369","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsically-motivated-reinforcement-2","title":"Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation","date":"2022-09-17","arxiv_id":"2209.08228","repositories_listed":0,"syntology":null},{"url":null,"slug":"radio-rank-aware-divergence-metrics-to","title":"RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations","date":"2022-09-17","arxiv_id":"2209.13520","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-filter-bubbles-within-deep","title":"Mitigating Filter Bubbles within Deep Recommender Systems","date":"2022-09-16","arxiv_id":"2209.08180","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-liquid-rank-reputation-system","title":"Application of Liquid Rank Reputation System for Content Recommendation","date":"2022-09-15","arxiv_id":"2209.07641","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-role-of-bias-in-news-recommendation-in","title":"The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic","date":"2022-09-15","arxiv_id":"2209.07608","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-beam-search-for-initial-access","title":"Efficient Beam Search for Initial Access Using Collaborative Filtering","date":"2022-09-14","arxiv_id":"2209.06669","repositories_listed":0,"syntology":null},{"url":null,"slug":"solutions-to-preference-manipulation-in","title":"Solutions to preference manipulation in recommender systems require knowledge of meta-preferences","date":"2022-09-14","arxiv_id":"2209.11801","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-online-ranking-with-fairness-of-exposure","title":"Fast online ranking with fairness of exposure","date":"2022-09-13","arxiv_id":"2209.13019","repositories_listed":0,"syntology":null},{"url":null,"slug":"inclusive-ethical-design-for-recommender","title":"Inclusive Ethical Design for Recommender Systems","date":"2022-09-13","arxiv_id":"2209.13021","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-challenge-based-survey-of-e-recruitment","title":"A challenge-based survey of e-recruitment recommendation systems","date":"2022-09-12","arxiv_id":"2209.05112","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-faithfulness-and-coherence-of-language","title":"On Faithfulness and Coherence of Language Explanations for Recommendation Systems","date":"2022-09-12","arxiv_id":"2209.05409","repositories_listed":0,"syntology":null},{"url":null,"slug":"ordinal-graph-gamma-belief-network-for-social","title":"Ordinal Graph Gamma Belief Network for Social Recommender Systems","date":"2022-09-12","arxiv_id":"2209.05106","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-isn-t-always-fair-candidate-set","title":"Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems","date":"2022-09-12","arxiv_id":"2209.05000","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-machine-learning-for-online","title":"Application of Machine Learning for Online Reputation Systems","date":"2022-09-10","arxiv_id":"2209.04650","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-intervention-for-fairness-in-multi","title":"Causal Intervention for Fairness in Multi-behavior Recommendation","date":"2022-09-10","arxiv_id":"2209.04589","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-matrix-factorisation-for-large-scale","title":"Fair Matrix Factorisation for Large-Scale Recommender Systems","date":"2022-09-09","arxiv_id":"2209.04394","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-and-powerful-architecture-for","title":"Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions","date":"2022-09-09","arxiv_id":"2209.04185","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-stakeholder-centered-view-on-fairness-in","title":"A Stakeholder-Centered View on Fairness in Music Recommender Systems","date":"2022-09-08","arxiv_id":"2209.06126","repositories_listed":0,"syntology":null},{"url":null,"slug":"ethical-and-social-considerations-in","title":"Ethical and Social Considerations in Automatic Expert Identification and People Recommendation in Organizational Knowledge Management Systems","date":"2022-09-08","arxiv_id":"2209.03819","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-on-applications-of-neurosymbolic","title":"Survey on Applications of Neurosymbolic Artificial Intelligence","date":"2022-09-08","arxiv_id":"2209.12618","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-users-aren-t-alright-dangerous-mental","title":"The Users Aren't Alright: Dangerous Mental Illness Behaviors and Recommendations","date":"2022-09-08","arxiv_id":"2209.03941","repositories_listed":0,"syntology":null},{"url":null,"slug":"who-pays-personalization-bossiness-and-the","title":"Who Pays? Personalization, Bossiness and the Cost of Fairness","date":"2022-09-08","arxiv_id":"2209.04043","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematical-evaluation-for-next-basket","title":"A Systematical Evaluation for Next-Basket Recommendation Algorithms","date":"2022-09-07","arxiv_id":"2209.02892","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scalable-recommendation-engine-for-new","title":"A Scalable Recommendation Engine for New Users and Items","date":"2022-09-06","arxiv_id":"2209.06128","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-conversational-preference","title":"Hierarchical Conversational Preference Elicitation with Bandit Feedback","date":"2022-09-06","arxiv_id":"2209.06129","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-consumer-fairness-objectives","title":"Matching Consumer Fairness Objectives & Strategies for RecSys","date":"2022-09-06","arxiv_id":"2209.02662","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-recommendation-system-based-on-mind-1","title":"User recommendation system based on MIND dataset","date":"2022-09-06","arxiv_id":"2209.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-brief-history-of-recommender-systems","title":"A Brief History of Recommender Systems","date":"2022-09-05","arxiv_id":"2209.01860","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-graph-contrastive-learning-for","title":"Disentangled Graph Contrastive Learning for Review-based Recommendation","date":"2022-09-04","arxiv_id":"2209.01524","repositories_listed":0,"syntology":null},{"url":null,"slug":"exposure-aware-recommendation-using","title":"Exposure-Aware Recommendation using Contextual Bandits","date":"2022-09-04","arxiv_id":"2209.01665","repositories_listed":0,"syntology":null},{"url":null,"slug":"future-gradient-descent-for-adapting-the","title":"Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems","date":"2022-09-02","arxiv_id":"2209.01143","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-generative-embeddings-using-an","title":"Sample Efficient Learning of Factored Embeddings of Tensor Fields","date":"2022-09-01","arxiv_id":"2209.00372","repositories_listed":0,"syntology":null}],"record_sha256":"f209ccedf51bd9986c2113045d719de443e6697c59720fa87f38cfd02e0477c5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}