{"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/50","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":50,"pages_in_order":61,"rows_per_page":100,"rows":[4901,5000],"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/49","next":"/task/recommendation-systems/papers/51","papers":[{"url":null,"slug":"facets-of-fairness-in-search-and","title":"Facets of Fairness in Search and Recommendation","date":"2020-07-16","arxiv_id":"2008.01194","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-distributed-bandits-for-online","title":"Fast Distributed Bandits for Online Recommendation Systems","date":"2020-07-16","arxiv_id":"2007.08061","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-based-recommendations-for-radio","title":"Content-based Recommendations for Radio Stations with Deep Learned Audio Fingerprints","date":"2020-07-15","arxiv_id":"2007.07486","repositories_listed":0,"syntology":null},{"url":null,"slug":"presentation-of-a-recommender-system-with","title":"Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens","date":"2020-07-15","arxiv_id":"2008.01192","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommender-systems-for-the-internet-of","title":"Recommender Systems for the Internet of Things: A Survey","date":"2020-07-14","arxiv_id":"2007.06758","repositories_listed":0,"syntology":null},{"url":"/paper/explainable-recommendation-via-interpretable","slug":"explainable-recommendation-via-interpretable","title":"Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability","date":"2020-07-12","arxiv_id":"2007.06133","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/explainable-recommendation-via-interpretable#ran","syntology_url":"https://syntology.ai/paper/2007.06133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06133"}},"official":null}},{"url":null,"slug":"graph-factorization-machines-for-cross-domain","title":"Graph Factorization Machines for Cross-Domain Recommendation","date":"2020-07-12","arxiv_id":"2007.05911","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-future-merchant-transaction-prediction","title":"Multi-future Merchant Transaction Prediction","date":"2020-07-10","arxiv_id":"2007.05303","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-review-on-context-aware","title":"A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings","date":"2020-07-09","arxiv_id":"2007.04782","repositories_listed":0,"syntology":null},{"url":null,"slug":"disco-pal-diachronic-spanish-sonnet-corpus","title":"DISCO PAL: Diachronic Spanish Sonnet Corpus with Psychological and Affective Labels","date":"2020-07-09","arxiv_id":"2007.04626","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-dpps-via-sampling-beyond-hkpv","title":"Learning from DPPs via Sampling: Beyond HKPV and symmetry","date":"2020-07-08","arxiv_id":"2007.04287","repositories_listed":0,"syntology":null},{"url":null,"slug":"mrif-multi-resolution-interest-fusion-for","title":"MRIF: Multi-resolution Interest Fusion for Recommendation","date":"2020-07-08","arxiv_id":"2007.07084","repositories_listed":0,"syntology":null},{"url":null,"slug":"pinnersage-multi-modal-user-embedding","title":"PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest","date":"2020-07-07","arxiv_id":"2007.03634","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-multi-agent-multi-armed-bandits","title":"Robust Multi-Agent Multi-Armed Bandits","date":"2020-07-07","arxiv_id":"2007.03812","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-variational-inference-1","title":"Meta-Learning Divergences of Variational Inference","date":"2020-07-06","arxiv_id":"2007.02912","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-manifold-learning-for-large-scale","title":"Multi-Manifold Learning for Large-scale Targeted Advertising System","date":"2020-07-05","arxiv_id":"2007.02334","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-min-max-reformulations-of-linear","title":"Novel min-max reformulations of Linear Inverse Problems","date":"2020-07-05","arxiv_id":"2007.02448","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-threats-against-federated-matrix","title":"Privacy Threats Against Federated Matrix Factorization","date":"2020-07-03","arxiv_id":"2007.01587","repositories_listed":0,"syntology":null},{"url":null,"slug":"psychfm-predicting-your-next-gamble","title":"PsychFM: Predicting your next gamble","date":"2020-07-03","arxiv_id":"2007.01833","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-based-emotion-aware-recommender","title":"Text-based Emotion Aware Recommender","date":"2020-07-03","arxiv_id":"2007.01455","repositories_listed":0,"syntology":null},{"url":null,"slug":"hardware-acceleration-of-sparse-and-irregular","title":"Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights","date":"2020-07-02","arxiv_id":"2007.00864","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupling-learning-of-complex-interactions","title":"Coupling Learning of Complex Interactions","date":"2020-07-01","arxiv_id":"2007.13534","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-commerce-and-sentiment-analysis-predicting","title":"e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-path-reasoning-on-graph-for","title":"Interactive Path Reasoning on Graph for Conversational Recommendation","date":"2020-07-01","arxiv_id":"2007.00194","repositories_listed":0,"syntology":null},{"url":null,"slug":"item-based-collaborative-filtering-with-bert","title":"Item-based Collaborative Filtering with BERT","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-iid-recommender-systems-a-review-and","title":"Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting","date":"2020-07-01","arxiv_id":"2007.07217","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-post-hoc-causal-explanations-for","title":"Learning Post-Hoc Causal Explanations for Recommendation","date":"2020-06-30","arxiv_id":"2006.16977","repositories_listed":0,"syntology":null},{"url":"/paper/tfnet-multi-semantic-feature-interaction-for","slug":"tfnet-multi-semantic-feature-interaction-for","title":"TFNet: Multi-Semantic Feature Interaction for CTR Prediction","date":"2020-06-29","arxiv_id":"2006.15939","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-neural-interaction","title":"Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction","date":"2020-06-29","arxiv_id":"2007.06434","repositories_listed":0,"syntology":null},{"url":null,"slug":"hop-sampling-a-simple-regularized-graph","title":"Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments","date":"2020-06-26","arxiv_id":"2006.14897","repositories_listed":0,"syntology":null},{"url":null,"slug":"memory-efficient-embedding-for","title":"Memory-efficient Embedding for Recommendations","date":"2020-06-26","arxiv_id":"2006.14827","repositories_listed":0,"syntology":null},{"url":null,"slug":"mood-based-on-car-music-recommendations","title":"Mood-based On-Car Music Recommendations","date":"2020-06-25","arxiv_id":"2006.14279","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-fairness-in-two-sided","title":"A Framework for Fairness in Two-Sided Marketplaces","date":"2020-06-23","arxiv_id":"2006.12756","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-assessment-and-mitigation-strategies","title":"Achieving Fairness via Post-Processing in Web-Scale Recommender Systems","date":"2020-06-19","arxiv_id":"2006.11350","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-knowledge-enhanced-recommendation-model","title":"A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention","date":"2020-06-18","arxiv_id":"2006.10233","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-recommender-system-via-knowledge","title":"Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning","date":"2020-06-18","arxiv_id":"2006.10389","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-by-repetition-stochastic-multi-armed","title":"Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect","date":"2020-06-18","arxiv_id":"2006.10356","repositories_listed":0,"syntology":null},{"url":null,"slug":"dcaf-a-dynamic-computation-allocation","title":"DCAF: A Dynamic Computation Allocation Framework for Online Serving System","date":"2020-06-17","arxiv_id":"2006.09684","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-feature-selection-to-unhide","title":"Deep Learning feature selection to unhide demographic recommender systems factors","date":"2020-06-17","arxiv_id":"2006.12379","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-intelligent-group-event-recommendation","title":"An Intelligent Group Event Recommendation System in Social networks","date":"2020-06-16","arxiv_id":"2006.08893","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-review-and-taxonomy-of","title":"A systematic review and taxonomy of explanations in decision support and recommender systems","date":"2020-06-15","arxiv_id":"2006.08672","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-decision-support-system-for","title":"Intelligent Decision Support System for Updating Control Plans","date":"2020-06-15","arxiv_id":"2006.08153","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-bandits-revisited","title":"Latent Bandits Revisited","date":"2020-06-15","arxiv_id":"2006.08714","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-federated-recommendation-system","title":"Robust Federated Recommendation System","date":"2020-06-15","arxiv_id":"2006.08259","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-profiling-from-reviews-for-accurate-time","title":"User Profiling from Reviews for Accurate Time-Based Recommendations","date":"2020-06-15","arxiv_id":"2006.08805","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-and-detection-on","title":"Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems","date":"2020-06-14","arxiv_id":"2006.07934","repositories_listed":0,"syntology":null},{"url":null,"slug":"amer-automatic-behavior-modeling-and","title":"AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System","date":"2020-06-10","arxiv_id":"2006.05933","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-reinforcement-learning","title":"Self-Supervised Reinforcement Learning for Recommender Systems","date":"2020-06-10","arxiv_id":"2006.05779","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-with-multi-layer-embeddings-for","title":"Training with Multi-Layer Embeddings for Model Reduction","date":"2020-06-10","arxiv_id":"2006.05623","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepfair-deep-learning-for-improving-fairness","title":"DeepFair: Deep Learning for Improving Fairness in Recommender Systems","date":"2020-06-09","arxiv_id":"2006.05255","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-neural-input-search-for","title":"Differentiable Neural Input Search for Recommender Systems","date":"2020-06-08","arxiv_id":"2006.04466","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-truth-from-only-one-side-of-the","title":"Learning the Truth From Only One Side of the Story","date":"2020-06-08","arxiv_id":"2006.04858","repositories_listed":0,"syntology":null},{"url":null,"slug":"mc2g-an-efficient-algorithm-for-matrix","title":"MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs","date":"2020-06-08","arxiv_id":"2006.04373","repositories_listed":0,"syntology":null},{"url":null,"slug":"connecting-user-and-item-perspectives-in","title":"Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation","date":"2020-06-07","arxiv_id":"2006.04275","repositories_listed":0,"syntology":null},{"url":null,"slug":"equality-of-learning-opportunity-in","title":"Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations","date":"2020-06-07","arxiv_id":"2006.04282","repositories_listed":0,"syntology":null},{"url":"/paper/feature-interaction-based-neural-network-for","slug":"feature-interaction-based-neural-network-for","title":"Feature Interaction based Neural Network for Click-Through Rate Prediction","date":"2020-06-07","arxiv_id":"2006.05312","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-federated-learning-to-fog-learning","title":"From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks","date":"2020-06-07","arxiv_id":"2006.03594","repositories_listed":0,"syntology":null},{"url":null,"slug":"interplay-between-upsampling-and","title":"Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems","date":"2020-06-07","arxiv_id":"2006.04279","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-layer-graph-convolutional-networks-for","title":"Single-Layer Graph Convolutional Networks For Recommendation","date":"2020-06-07","arxiv_id":"2006.04164","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-auto-encoder-for-recommender","title":"Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems","date":"2020-06-05","arxiv_id":"2006.03573","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-explainable-recommendation","title":"Fairness-Aware Explainable Recommendation over Knowledge Graphs","date":"2020-06-03","arxiv_id":"2006.02046","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-stationary-bandits-with-intermediate","title":"Non-Stationary Delayed Bandits with Intermediate Observations","date":"2020-06-03","arxiv_id":"2006.02119","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-in-the-loop-using-a-photonics-co","title":"Light-in-the-loop: using a photonics co-processor for scalable training of neural networks","date":"2020-06-02","arxiv_id":"2006.01475","repositories_listed":0,"syntology":null},{"url":null,"slug":"submodular-bandit-problem-under-multiple","title":"Submodular Bandit Problem Under Multiple Constraints","date":"2020-06-01","arxiv_id":"2006.00661","repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-modeling-intra-and-inter-transaction","title":"Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation","date":"2020-05-30","arxiv_id":"2006.04530","repositories_listed":0,"syntology":null},{"url":null,"slug":"operationalizing-the-legal-principle-of-data","title":"Operationalizing the Legal Principle of Data Minimization for Personalization","date":"2020-05-28","arxiv_id":"2005.13718","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-exploratory-study-of-hierarchical-fuzzy","title":"An Exploratory Study of Hierarchical Fuzzy Systems Approach in Recommendation System","date":"2020-05-27","arxiv_id":"2005.14026","repositories_listed":0,"syntology":null},{"url":null,"slug":"atbrg-adaptive-target-behavior-relational","title":"ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation","date":"2020-05-25","arxiv_id":"2005.12002","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-item-recommendation-and-attribute","title":"Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach","date":"2020-05-25","arxiv_id":"2005.12021","repositories_listed":0,"syntology":null},{"url":null,"slug":"opportunistic-multi-aspect-fairness-through","title":"Opportunistic Multi-aspect Fairness through Personalized Re-ranking","date":"2020-05-21","arxiv_id":"2005.12974","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-via-contextual-invariants","title":"Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation","date":"2020-05-21","arxiv_id":"2005.10473","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-debiased-candidate","title":"Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems","date":"2020-05-20","arxiv_id":"2005.12964","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-reinforcement-learning-algorithms","title":"A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments","date":"2020-05-19","arxiv_id":"2005.10619","repositories_listed":0,"syntology":null},{"url":null,"slug":"edgerec-recommender-system-on-edge-in-mobile","title":"EdgeRec: Recommender System on Edge in Mobile Taobao","date":"2020-05-18","arxiv_id":"2005.08416","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-sequential-recommender-via-time-aware","title":"Sequential Recommender via Time-aware Attentive Memory Network","date":"2020-05-18","arxiv_id":"2005.08598","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-to-enhance-pure","title":"A Hybrid Approach to Enhance Pure Collaborative Filtering based on Content Feature Relationship","date":"2020-05-17","arxiv_id":"2005.08148","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-gender-bias-in-machine-learning","title":"Mitigating Gender Bias in Machine Learning Data Sets","date":"2020-05-14","arxiv_id":"2005.06898","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundations-and-modelling-of-dynamic-networks","title":"Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey","date":"2020-05-13","arxiv_id":"2005.07496","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-embedding-fusion-based","title":"Multi-modal Embedding Fusion-based Recommender","date":"2020-05-13","arxiv_id":"2005.06331","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-stackelberg-signaling-and-its-impact-on","title":"Framing Effects on Strategic Information Design under Receiver Distrust and Unknown State","date":"2020-05-12","arxiv_id":"2005.05516","repositories_listed":0,"syntology":null},{"url":null,"slug":"keen2act-activity-recommendation-in-online","title":"Keen2Act: Activity Recommendation in Online Social Collaborative Platforms","date":"2020-05-11","arxiv_id":"2005.04833","repositories_listed":0,"syntology":null},{"url":null,"slug":"socialtrans-a-deep-sequential-model-with","title":"SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems","date":"2020-05-09","arxiv_id":"2005.04361","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-algorithms-for-hierarchical","title":"Fair Algorithms for Hierarchical Agglomerative Clustering","date":"2020-05-07","arxiv_id":"2005.03197","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-for-graph-datasets-via-gumbel","title":"Community Detection Clustering via Gumbel Softmax","date":"2020-05-05","arxiv_id":"2005.02372","repositories_listed":0,"syntology":null},{"url":null,"slug":"reward-constrained-interactive-recommendation","title":"Reward Constrained Interactive Recommendation with Natural Language Feedback","date":"2020-05-04","arxiv_id":"2005.01618","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairmatch-a-graph-based-approach-for","title":"FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems","date":"2020-05-03","arxiv_id":"2005.01148","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-explicit-and-implicit","title":"A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-autoencoders-for-highly","title":"Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-hierarchical-graph-convolutional","title":"A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering","date":"2020-04-30","arxiv_id":"2004.14734","repositories_listed":0,"syntology":null},{"url":null,"slug":"ds-facto-doubly-separable-factorization","title":"DS-FACTO: Doubly Separable Factorization Machines","date":"2020-04-29","arxiv_id":"2004.13940","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-linear-bandit-for-seasonal-environments","title":"A Linear Bandit for Seasonal Environments","date":"2020-04-28","arxiv_id":"2004.13576","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-conversational-recommender-systems-a-new","title":"Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems","date":"2020-04-28","arxiv_id":"2004.13245","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-recommender-systems","title":"Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline","date":"2020-04-28","arxiv_id":"2004.13715","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-recommendation-of-pois-to-people","title":"Personalized Recommendation of PoIs to People with Autism","date":"2020-04-27","arxiv_id":"2004.12733","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-session-based-song-recommendation-approach","title":"A session-based song recommendation approach involving user characterization along the play power-law distribution","date":"2020-04-25","arxiv_id":"2004.13007","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-in-deep-learning-a-survey","title":"Privacy in Deep Learning: A Survey","date":"2020-04-25","arxiv_id":"2004.12254","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-recommendation-diversity-by","title":"Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts","date":"2020-04-24","arxiv_id":"2004.11662","repositories_listed":0,"syntology":null},{"url":null,"slug":"alleviating-the-recommendation-bias-via-rank","title":"Alleviating the recommendation bias via rank aggregation","date":"2020-04-22","arxiv_id":"2004.10393","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-learning-approaches-to-recommender","title":"Graph Learning Approaches to Recommender Systems: A Review","date":"2020-04-22","arxiv_id":"2004.11718","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-optimizing-for-clicks-incorporating","title":"Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation","date":"2020-04-21","arxiv_id":"2004.09980","repositories_listed":0,"syntology":null}],"record_sha256":"df4271a00f77fe8a24c31a6387a4f1c28d47c648908c0f2eeff6941ae26e1bf2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}