{"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/45","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":45,"pages_in_order":61,"rows_per_page":100,"rows":[4401,4500],"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/44","next":"/task/recommendation-systems/papers/46","papers":[{"url":null,"slug":"peek-a-large-dataset-of-learner-engagement","title":"PEEK: A Large Dataset of Learner Engagement with Educational Videos","date":"2021-09-03","arxiv_id":"2109.03154","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-does-the-user-s-knowledge-of-the","title":"How does the User's Knowledge of the Recommender Influence their Behavior?","date":"2021-09-02","arxiv_id":"2109.00982","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-n-recommendation-with-counterfactual-user","title":"Top-N Recommendation with Counterfactual User Preference Simulation","date":"2021-09-02","arxiv_id":"2109.02444","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-collaborative-filtering-to-model","title":"Using Collaborative Filtering to Model Argument Selection","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"position-based-hash-embeddings-for-scaling","title":"Position-based Hash Embeddings For Scaling Graph Neural Networks","date":"2021-08-31","arxiv_id":"2109.00101","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-on-the-cold-start-problem-model","title":"Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems","date":"2021-08-31","arxiv_id":"2108.13592","repositories_listed":0,"syntology":null},{"url":null,"slug":"photos-are-all-you-need-for-reciprocal","title":"Photos Are All You Need for Reciprocal Recommendation in Online Dating","date":"2021-08-26","arxiv_id":"2108.11714","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-self-attentive-sequential","title":"Lightweight Self-Attentive Sequential Recommendation","date":"2021-08-25","arxiv_id":"2108.11333","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommendation-system-simulations-a","title":"Recommendation System Simulations: A Discussion of Two Key Challenges","date":"2021-08-25","arxiv_id":"2109.02475","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-longitudinal-dynamics-of","title":"Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation","date":"2021-08-25","arxiv_id":"2108.11068","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-code-based-hash-embedding-for-web","title":"Binary Code based Hash Embedding for Web-scale Applications","date":"2021-08-24","arxiv_id":"2109.02471","repositories_listed":0,"syntology":null},{"url":null,"slug":"pasto-strategic-parameter-optimization-in","title":"PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic","date":"2021-08-20","arxiv_id":"2108.09076","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-to-optimize-lifetime","title":"Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation","date":"2021-08-20","arxiv_id":"2108.09141","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-entire-space-multi-task-models","title":"An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction","date":"2021-08-18","arxiv_id":"2108.13475","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-and-secure-federated-recommendation","title":"Practical and Secure Federated Recommendation with Personalized Masks","date":"2021-08-18","arxiv_id":"2109.02464","repositories_listed":0,"syntology":null},{"url":null,"slug":"sifn-a-sentiment-aware-interactive-fusion","title":"SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation","date":"2021-08-18","arxiv_id":"2108.08022","repositories_listed":0,"syntology":null},{"url":null,"slug":"moi-mixer-improving-mlp-mixer-with-multi","title":"MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation","date":"2021-08-17","arxiv_id":"2108.07505","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-item-popularity-bias-of-music","title":"Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?","date":"2021-08-16","arxiv_id":"2108.06973","repositories_listed":0,"syntology":null},{"url":null,"slug":"adagnn-a-multi-modal-latent-representation","title":"AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting","date":"2021-08-14","arxiv_id":"2108.06452","repositories_listed":0,"syntology":null},{"url":null,"slug":"linkteller-recovering-private-edges-from","title":"LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis","date":"2021-08-14","arxiv_id":"2108.06504","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-scale-free-graphs-for-knowledge","title":"Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation","date":"2021-08-14","arxiv_id":"2108.06468","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-for-personalized","title":"Incremental Learning for Personalized Recommender Systems","date":"2021-08-13","arxiv_id":"2108.13299","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-recommendations-a-tutorial","title":"Multi-Objective Recommendations: A Tutorial","date":"2021-08-13","arxiv_id":"2108.06367","repositories_listed":0,"syntology":null},{"url":null,"slug":"logit-attenuating-weight-normalization","title":"Logit Attenuating Weight Normalization","date":"2021-08-12","arxiv_id":"2108.05839","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-graph-collaborative-filtering","title":"Localized Graph Collaborative Filtering","date":"2021-08-10","arxiv_id":"2108.04475","repositories_listed":0,"syntology":null},{"url":null,"slug":"poso-personalized-cold-start-modules-for","title":"POSO: Personalized Cold Start Modules for Large-scale Recommender Systems","date":"2021-08-10","arxiv_id":"2108.04690","repositories_listed":0,"syntology":null},{"url":"/paper/truman-trope-understanding-in-movies-and","slug":"truman-trope-understanding-in-movies-and","title":"TrUMAn: Trope Understanding in Movies and Animations","date":"2021-08-10","arxiv_id":"2108.04542","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-cross-domain-recommendation","title":"A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions","date":"2021-08-07","arxiv_id":"2108.03357","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-cascade-bandits-mitigating-exposure","title":"Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation","date":"2021-08-07","arxiv_id":"2108.03440","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-smart-and-defensive-human-machine-approach","title":"A Smart and Defensive Human-Machine Approach to Code Analysis","date":"2021-08-06","arxiv_id":"2108.03294","repositories_listed":0,"syntology":null},{"url":null,"slug":"itinerary-aware-personalized-deep-matching-at","title":"Itinerary-aware Personalized Deep Matching at Fliggy","date":"2021-08-05","arxiv_id":"2108.02343","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-elect","title":"Learning to Elect","date":"2021-08-05","arxiv_id":"2108.02768","repositories_listed":0,"syntology":null},{"url":null,"slug":"lhrm-a-lbs-based-heterogeneous-relations","title":"LHRM: A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform","date":"2021-08-05","arxiv_id":"2108.02344","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-model-integration-algorithm-for","title":"Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks","date":"2021-08-03","arxiv_id":"2108.01532","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hinge-loss-based-codebook-transfer-for","title":"A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data","date":"2021-08-02","arxiv_id":"2108.01473","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-user-demographics-based-on","title":"Predicting user demographics based on interest analysis","date":"2021-08-02","arxiv_id":"2108.01014","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-completion-using-geodesics-on-segre","title":"Tensor completion using geodesics on Segre manifolds","date":"2021-08-02","arxiv_id":"2108.00735","repositories_listed":0,"syntology":null},{"url":"/paper/the-circor-digiscope-dataset-from-murmur","slug":"the-circor-digiscope-dataset-from-murmur","title":"The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification","date":"2021-08-02","arxiv_id":"2108.00813","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogue-act-classification-for-augmentative","title":"Dialogue Act Classification for Augmentative and Alternative Communication","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-analysis-on-transparent","title":"An Empirical Analysis on Transparent Algorithmic Exploration in Recommender Systems","date":"2021-07-31","arxiv_id":"2108.00151","repositories_listed":0,"syntology":null},{"url":null,"slug":"indexability-and-rollout-policy-for-multi","title":"Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits","date":"2021-07-30","arxiv_id":"2108.00892","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-feature-factorization-for-recommender","title":"Sparse Feature Factorization for Recommender Systems with Knowledge Graphs","date":"2021-07-29","arxiv_id":"2107.14290","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-effects-of-adversarial","title":"Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality","date":"2021-07-29","arxiv_id":"2107.13876","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-multiclass-auc-theory-and","title":"Learning with Multiclass AUC: Theory and Algorithms","date":"2021-07-28","arxiv_id":"2107.13171","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-payload-optimization-method-for-federated","title":"A Payload Optimization Method for Federated Recommender Systems","date":"2021-07-27","arxiv_id":"2107.13078","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-reward-and-rank-signals-for-slate","title":"Combining Reward and Rank Signals for Slate Recommendation","date":"2021-07-26","arxiv_id":"2107.12455","repositories_listed":0,"syntology":null},{"url":null,"slug":"leaf-fm-a-learnable-feature-generation","title":"Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction","date":"2021-07-26","arxiv_id":"2107.12024","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-based-music-recommendation-evolution","title":"Content-driven Music Recommendation: Evolution, State of the Art, and Challenges","date":"2021-07-25","arxiv_id":"2107.11803","repositories_listed":0,"syntology":null},{"url":null,"slug":"restless-bandits-with-many-arms-beating-the","title":"Restless Bandits with Many Arms: Beating the Central Limit Theorem","date":"2021-07-25","arxiv_id":"2107.11911","repositories_listed":0,"syntology":null},{"url":null,"slug":"ready-for-emerging-threats-to-recommender","title":"Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack","date":"2021-07-22","arxiv_id":"2107.10457","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-are-you-optimizing-for-aligning","title":"What are you optimizing for? Aligning Recommender Systems with Human Values","date":"2021-07-22","arxiv_id":"2107.10939","repositories_listed":0,"syntology":null},{"url":null,"slug":"febr-expert-based-recommendation-framework","title":"FEBR: Expert-Based Recommendation Framework for beneficial and personalized content","date":"2021-07-17","arxiv_id":"2108.01455","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-knowledge-graph-embedding-and","title":"A Survey of Knowledge Graph Embedding and Their Applications","date":"2021-07-16","arxiv_id":"2107.07842","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-user-behaviour-in-research-paper","title":"Modeling User Behaviour in Research Paper Recommendation System","date":"2021-07-16","arxiv_id":"2107.07831","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-educational-system-for-personalized","title":"An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms","date":"2021-07-15","arxiv_id":"2107.07124","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-detecting-groups-based-on-textual","title":"Auto-detecting groups based on textual similarity for group recommendations","date":"2021-07-15","arxiv_id":"2107.07284","repositories_listed":0,"syntology":null},{"url":null,"slug":"next-item-recommendations-in-short-sessions","title":"Next-item Recommendations in Short Sessions","date":"2021-07-15","arxiv_id":"2107.07453","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-for-recommendations-at","title":"Online Learning for Recommendations at Grubhub","date":"2021-07-15","arxiv_id":"2107.07106","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommending-best-course-of-treatment-based","title":"Recommending best course of treatment based on similarities of prognostic markers","date":"2021-07-15","arxiv_id":"2107.07500","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-adaptive-knowledge-distillation-for","title":"Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search","date":"2021-07-15","arxiv_id":"2107.07173","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-benchmark-lottery","title":"The Benchmark Lottery","date":"2021-07-14","arxiv_id":"2107.07002","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-influential-users-in-unknown","title":"Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction","date":"2021-07-13","arxiv_id":"2107.05992","repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-recommend-items-to-wikidata","slug":"learning-to-recommend-items-to-wikidata","title":"Learning to Recommend Items to Wikidata Editors","date":"2021-07-13","arxiv_id":"2107.06423","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-step-critiquing-user-interface-for","title":"Multi-Step Critiquing User Interface for Recommender Systems","date":"2021-07-13","arxiv_id":"2107.06416","repositories_listed":0,"syntology":null},{"url":null,"slug":"sliding-spectrum-decomposition-for","title":"Sliding Spectrum Decomposition for Diversified Recommendation","date":"2021-07-12","arxiv_id":"2107.05204","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-recommender-systems-to-depolarize","title":"Designing Recommender Systems to Depolarize","date":"2021-07-11","arxiv_id":"2107.04953","repositories_listed":0,"syntology":null},{"url":null,"slug":"svp-cf-selection-via-proxy-for-collaborative","title":"SVP-CF: Selection via Proxy for Collaborative Filtering Data","date":"2021-07-11","arxiv_id":"2107.04984","repositories_listed":0,"syntology":null},{"url":"/paper/transformers-with-multi-modal-features-and","slug":"transformers-with-multi-modal-features-and","title":"Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation","date":"2021-07-11","arxiv_id":"2107.05124","repositories_listed":0,"syntology":null},{"url":null,"slug":"propagation-aware-social-recommendation-by","title":"Propagation-aware Social Recommendation by Transfer Learning","date":"2021-07-10","arxiv_id":"2107.04846","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-pre-training-for-enhancing","title":"Graph Neural Pre-training for Enhancing Recommendations using Side Information","date":"2021-07-08","arxiv_id":"2107.03936","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-based-approach-for-mitigating-multi","title":"A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems","date":"2021-07-07","arxiv_id":"2107.03415","repositories_listed":0,"syntology":null},{"url":null,"slug":"note-solution-for-kdd-cup-2021-wikikg90m-lsc","title":"NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC","date":"2021-07-05","arxiv_id":"2107.01892","repositories_listed":0,"syntology":null},{"url":null,"slug":"attribute-aware-explainable-complementary","title":"Attribute-aware Explainable Complementary Clothing Recommendation","date":"2021-07-04","arxiv_id":"2107.01655","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-complex-users-preferences-for","title":"Learning Complex Users' Preferences for Recommender Systems","date":"2021-07-04","arxiv_id":"2107.01529","repositories_listed":0,"syntology":null},{"url":null,"slug":"acai-ascent-similarity-caching-with","title":"Ascent Similarity Caching with Approximate Indexes","date":"2021-07-02","arxiv_id":"2107.00957","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-cross-session-information-for","title":"Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks","date":"2021-07-02","arxiv_id":"2107.00852","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-positional-information-for-session","title":"Exploiting Positional Information for Session-based Recommendation","date":"2021-07-02","arxiv_id":"2107.00846","repositories_listed":0,"syntology":null},{"url":null,"slug":"general-board-game-concepts","title":"General Board Game Concepts","date":"2021-07-02","arxiv_id":"2107.01078","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-based-recommender-system-for-job-to","title":"Embedding-based Recommender System for Job to Candidate Matching on Scale","date":"2021-07-01","arxiv_id":"2107.00221","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-use-of-bandit-algorithms-in-intelligent","title":"The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems","date":"2021-07-01","arxiv_id":"2107.00161","repositories_listed":0,"syntology":null},{"url":null,"slug":"intent-disentanglement-and-feature-self","title":"Intent Disentanglement and Feature Self-supervision for Novel Recommendation","date":"2021-06-28","arxiv_id":"2106.14388","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-component-interactions-in-two-stage","title":"On component interactions in two-stage recommender systems","date":"2021-06-28","arxiv_id":"2106.14979","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-based-adaptive-tree-for-multimodal","title":"Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects","date":"2021-06-27","arxiv_id":"2106.14174","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-accuracy-and-fairness-for","title":"Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning","date":"2021-06-25","arxiv_id":"2106.13386","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-infused-policy-gradients-with-upper","title":"Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits","date":"2021-06-25","arxiv_id":"2106.13895","repositories_listed":0,"syntology":null},{"url":null,"slug":"fund2vec-mutual-funds-similarity-using-graph","title":"Fund2Vec: Mutual Funds Similarity using Graph Learning","date":"2021-06-24","arxiv_id":"2106.12987","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-transformer-based-sequential","title":"Improving Transformer-based Sequential Recommenders through Preference Editing","date":"2021-06-23","arxiv_id":"2106.12120","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-stereotyping-problem-in-collaboratively","title":"The Stereotyping Problem in Collaboratively Filtered Recommender Systems","date":"2021-06-23","arxiv_id":"2106.12622","repositories_listed":0,"syntology":null},{"url":null,"slug":"banditmf-multi-armed-bandit-based-matrix","title":"BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System","date":"2021-06-21","arxiv_id":"2106.10898","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-optimisation-for-a-deep-learning","title":"Data Optimisation for a Deep Learning Recommender System","date":"2021-06-21","arxiv_id":"2106.11218","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-shaping-a-modeling-approach-to","title":"Feedback Shaping: A Modeling Approach to Nurture Content Creation","date":"2021-06-21","arxiv_id":"2106.11312","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-network-embedding-in-apache-spark","title":"Large-Scale Network Embedding in Apache Spark","date":"2021-06-20","arxiv_id":"2106.10620","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-of-interest-recommender-systems-a","title":"Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective","date":"2021-06-18","arxiv_id":"2106.10069","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-fairness-aware-information-retrieval","title":"FAIR: Fairness-Aware Information Retrieval Evaluation","date":"2021-06-16","arxiv_id":"2106.08527","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-news-recommendation-a-survey","title":"Personalized News Recommendation: Methods and Challenges","date":"2021-06-16","arxiv_id":"2106.08934","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-distillation-for-recommender-system","title":"Topology Distillation for Recommender System","date":"2021-06-16","arxiv_id":"2106.08700","repositories_listed":0,"syntology":null},{"url":null,"slug":"mean-embeddings-with-test-time-data","title":"Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations","date":"2021-06-15","arxiv_id":"2106.08038","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-specific-adaptive-fine-tuning-for-cross","title":"User-specific Adaptive Fine-tuning for Cross-domain Recommendations","date":"2021-06-15","arxiv_id":"2106.07864","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-data-specific-model-search-for","title":"Efficient Data-specific Model Search for Collaborative Filtering","date":"2021-06-14","arxiv_id":"2106.07453","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-domain-knowledge-into-health","title":"Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings","date":"2021-06-14","arxiv_id":"2106.07720","repositories_listed":0,"syntology":null}],"record_sha256":"07e595f66deb045a1c84383d391a1d72d112235e62f0d2ee01e931e5326375b8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}