{"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/49","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":49,"pages_in_order":61,"rows_per_page":100,"rows":[4801,4900],"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/48","next":"/task/recommendation-systems/papers/50","papers":[{"url":null,"slug":"automatic-extraction-of-agriculture-terms","title":"Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques","date":"2020-09-24","arxiv_id":"2009.11796","repositories_listed":0,"syntology":null},{"url":null,"slug":"tuning-word2vec-for-large-scale","title":"Tuning Word2vec for Large Scale Recommendation Systems","date":"2020-09-24","arxiv_id":"2009.12192","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-hybrid-model-for-recommendation","title":"A Deep Hybrid Model for Recommendation Systems","date":"2020-09-21","arxiv_id":"2009.09748","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandits-under-the-influence-extended-version","title":"Bandits Under The Influence (Extended Version)","date":"2020-09-21","arxiv_id":"2009.10135","repositories_listed":0,"syntology":null},{"url":null,"slug":"div2vec-diversity-emphasized-node-embedding","title":"div2vec: Diversity-Emphasized Node Embedding","date":"2020-09-21","arxiv_id":"2009.09588","repositories_listed":0,"syntology":null},{"url":null,"slug":"hotel-recommendation-system-based-on-user","title":"Hotel Recommendation System Based on User Profiles and Collaborative Filtering","date":"2020-09-21","arxiv_id":"2009.14045","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-transfer-via-pre-training-for","title":"Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect","date":"2020-09-19","arxiv_id":"2009.09226","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-online-behavior-in-recommender","title":"Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context","date":"2020-09-19","arxiv_id":"2009.08978","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-multi-session-website-fingerprinting-over","title":"On Multi-Session Website Fingerprinting over TLS Handshake","date":"2020-09-19","arxiv_id":"2009.09284","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-alignment-with-mixture-experts","title":"Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation","date":"2020-09-17","arxiv_id":"2009.09926","repositories_listed":0,"syntology":null},{"url":null,"slug":"planting-trees-at-the-right-places","title":"Planting trees at the right places: Recommending suitable sites for growing trees using algorithm fusion","date":"2020-09-17","arxiv_id":"2009.08002","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-opinion-mining-using-a-hybrid","title":"Arabic Opinion Mining Using a Hybrid Recommender System Approach","date":"2020-09-16","arxiv_id":"2009.07397","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-bandit-and-semi-bandit-making-the","title":"Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback","date":"2020-09-16","arxiv_id":"2009.07518","repositories_listed":0,"syntology":null},{"url":null,"slug":"stratified-and-time-aware-sampling-based","title":"Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations","date":"2020-09-15","arxiv_id":"2009.06824","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-wing-mixture-of-experts-for-streaming","title":"Double-Wing Mixture of Experts for Streaming Recommendations","date":"2020-09-14","arxiv_id":"2009.06327","repositories_listed":0,"syntology":null},{"url":null,"slug":"spoiled-for-choice-personalized","title":"Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach","date":"2020-09-13","arxiv_id":"2009.06108","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-citation-recommendation-strategy-based","title":"A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section","date":"2020-09-11","arxiv_id":"2009.08948","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-based-player-and-game-interaction","title":"Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting","date":"2020-09-11","arxiv_id":"2009.08947","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-news-recommender-system-dealing","title":"News Recommender System: A review of recent progress, challenges, and opportunities","date":"2020-09-10","arxiv_id":"2009.04964","repositories_listed":0,"syntology":null},{"url":null,"slug":"momentum-based-gradient-methods-in-multi","title":"Momentum-based Gradient Methods in Multi-Objective Recommendation","date":"2020-09-10","arxiv_id":"2009.04695","repositories_listed":0,"syntology":null},{"url":null,"slug":"presentation-a-trust-walker-for-rating","title":"Presentation a Trust Walker for rating prediction in Recommender System with Biased Random Walk: Effects of H-index Centrality, Similarity in Items and Friends","date":"2020-09-10","arxiv_id":"2009.04825","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-use-of-recommender-systems-in-web","title":"The use of Recommender Systems in web technology and an in-depth analysis of Cold State problem","date":"2020-09-10","arxiv_id":"2009.04780","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-identification-of-fair-auditors-to","title":"On the Identification of Fair Auditors to Evaluate Recommender Systems based on a Novel Non-Comparative Fairness Notion","date":"2020-09-09","arxiv_id":"2009.04383","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-cold-start-in-recommender-systems","title":"Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks","date":"2020-09-07","arxiv_id":"2009.03455","repositories_listed":0,"syntology":null},{"url":null,"slug":"and-the-winner-is-dynamic-lotteries-for-multi","title":"\"And the Winner Is...\": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation","date":"2020-09-05","arxiv_id":"2009.02590","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperfair-a-soft-approach-to-integrating","title":"HyperFair: A Soft Approach to Integrating Fairness Criteria","date":"2020-09-05","arxiv_id":"2009.08952","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-framework-for-fairness-in","title":"A General Framework for Fairness in Multistakeholder Recommendations","date":"2020-09-04","arxiv_id":"2009.02423","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-practical-incremental-method-to-train-deep","title":"A Practical Incremental Method to Train Deep CTR Models","date":"2020-09-04","arxiv_id":"2009.02147","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-preference-and-metric-learning","title":"Simultaneous Preference and Metric Learning from Paired Comparisons","date":"2020-09-04","arxiv_id":"2009.02302","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-should-i-not-follow-you-reasons-for-and","title":"Why should I not follow you? Reasons For and Reasons Against in Responsible Recommender Systems","date":"2020-09-03","arxiv_id":"2009.01953","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-fair-ranking-metrics","title":"Comparing Fair Ranking Metrics","date":"2020-09-02","arxiv_id":"2009.01311","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-latent-codes-interactive-fashion","title":"Exploiting Latent Codes: Interactive Fashion Product Generation, Similar Image Retrieval, and Cross-Category Recommendation using Variational Autoencoders","date":"2020-09-02","arxiv_id":"2009.01053","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-graph-neural-network-for","title":"Heterogeneous Graph Neural Network for Recommendation","date":"2020-09-02","arxiv_id":"2009.00799","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-fair-collaborative-filtering","title":"Neural Fair Collaborative Filtering","date":"2020-09-02","arxiv_id":"2009.08955","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploration-in-two-stage-recommender-systems","title":"Exploration in two-stage recommender systems","date":"2020-09-01","arxiv_id":"2009.08956","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-clicks-to-conversions-recommendation-for","title":"From Clicks to Conversions: Recommendation for long-term reward","date":"2020-09-01","arxiv_id":"2009.00497","repositories_listed":0,"syntology":null},{"url":null,"slug":"quaternion-based-self-attentive-long-short","title":"Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation","date":"2020-08-31","arxiv_id":"2008.13335","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-differntiable-ranking-metric-using-relaxed","title":"A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems","date":"2020-08-30","arxiv_id":"2008.13141","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-next-item-recommendation-recommending","title":"Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences","date":"2020-08-30","arxiv_id":"2008.13281","repositories_listed":0,"syntology":null},{"url":null,"slug":"blob-a-probabilistic-model-for-recommendation","title":"BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals","date":"2020-08-28","arxiv_id":"2008.12504","repositories_listed":0,"syntology":null},{"url":null,"slug":"premier-personalized-recommendation-for","title":"PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records","date":"2020-08-28","arxiv_id":"2008.13569","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-channel-hypergraph-collaborative","title":"Dual Channel Hypergraph Collaborative Filtering","date":"2020-08-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dve-dynamic-variational-embeddings-with","title":"DVE: Dynamic Variational Embeddings with Applications in Recommender Systems","date":"2020-08-27","arxiv_id":"2009.08962","repositories_listed":0,"syntology":null},{"url":null,"slug":"at-your-service-coffee-beans-recommendation","title":"At Your Service: Coffee Beans Recommendation From a Robot Assistant","date":"2020-08-26","arxiv_id":"2008.13585","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-aware-music-recommender-systems-modeling","title":"Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach","date":"2020-08-26","arxiv_id":"2008.11432","repositories_listed":0,"syntology":null},{"url":null,"slug":"lstm-networks-for-online-cross-network","title":"LSTM Networks for Online Cross-Network Recommendations","date":"2020-08-25","arxiv_id":"2008.10849","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-comprehensive-recommender-systems","title":"Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data","date":"2020-08-25","arxiv_id":"2008.13516","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-rank-weak-multi-objective","title":"Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling","date":"2020-08-24","arxiv_id":"2008.10277","repositories_listed":0,"syntology":null},{"url":null,"slug":"fatigue-aware-bandits-for-dependent-click","title":"Fatigue-aware Bandits for Dependent Click Models","date":"2020-08-22","arxiv_id":"2008.09733","repositories_listed":0,"syntology":null},{"url":null,"slug":"lt4rec-a-lottery-ticket-hypothesis-based","title":"NCS4CVR: Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction","date":"2020-08-22","arxiv_id":"2008.09872","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-recommender-systems-via-resolving","title":"Explainable Recommender Systems via Resolving Learning Representations","date":"2020-08-21","arxiv_id":"2008.09316","repositories_listed":0,"syntology":null},{"url":null,"slug":"offline-contextual-multi-armed-bandits-for","title":"Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation","date":"2020-08-21","arxiv_id":"2008.09472","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-connection-between-popularity-bias","title":"The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation","date":"2020-08-21","arxiv_id":"2008.09273","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-commerce-recommendation-with-weighted","title":"E-commerce Recommendation with Weighted Expected Utility","date":"2020-08-19","arxiv_id":"2008.08302","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-historical-interaction-data-for","title":"Leveraging Historical Interaction Data for Improving Conversational Recommender System","date":"2020-08-19","arxiv_id":"2008.08247","repositories_listed":0,"syntology":null},{"url":null,"slug":"popularity-bias-in-recommendation-a-multi","title":"Popularity Bias in Recommendation: A Multi-stakeholder Perspective","date":"2020-08-19","arxiv_id":"2008.08551","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-tuple-compatibility-for-conditional","title":"Learning Tuple Compatibility for Conditional OutfitRecommendation","date":"2020-08-18","arxiv_id":"2008.08189","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-point-estimate-inferring-ensemble","title":"Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems","date":"2020-08-17","arxiv_id":"2008.07032","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-item-representation-for","title":"Disentangled Item Representation for Recommender Systems","date":"2020-08-17","arxiv_id":"2008.07178","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-put-users-in-control-of-their-data-via","title":"How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank","date":"2020-08-17","arxiv_id":"2008.07192","repositories_listed":0,"syntology":null},{"url":null,"slug":"accountable-off-policy-evaluation-with-kernel","title":"Accountable Off-Policy Evaluation With Kernel Bellman Statistics","date":"2020-08-15","arxiv_id":"2008.06668","repositories_listed":0,"syntology":null},{"url":null,"slug":"preferential-bayesian-optimisation-with-skew","title":"Preferential Bayesian optimisation with Skew Gaussian Processes","date":"2020-08-15","arxiv_id":"2008.06677","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtbrn-multiplex-target-behavior-relation","title":"MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction","date":"2020-08-13","arxiv_id":"2008.05673","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-information-retrieval-from","title":"Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering","date":"2020-08-12","arxiv_id":"2008.05399","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-recommendation-with-metric-models","title":"Sequential recommendation with metric models based on frequent sequences","date":"2020-08-12","arxiv_id":"2008.05587","repositories_listed":0,"syntology":null},{"url":null,"slug":"dream-a-dynamic-relational-aware-model-for","title":"DREAM: A Dynamic Relational-Aware Model for Social Recommendation","date":"2020-08-11","arxiv_id":"2008.04579","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-learning-for-the-causal-effect-of","title":"Unbiased Learning for the Causal Effect of Recommendation","date":"2020-08-11","arxiv_id":"2008.04563","repositories_listed":0,"syntology":null},{"url":null,"slug":"scientific-paper-recommendation-a-survey","title":"Scientific Paper Recommendation: A Survey","date":"2020-08-10","arxiv_id":"2008.13538","repositories_listed":0,"syntology":null},{"url":null,"slug":"socially-aware-conference-participant","title":"Socially-Aware Conference Participant Recommendation with Personality Traits","date":"2020-08-09","arxiv_id":"2008.04653","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-heterogeneous-transfer-learning","title":"Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval","date":"2020-08-07","arxiv_id":"2008.02930","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-architectures-stability","title":"Graph Neural Networks: Architectures, Stability and Transferability","date":"2020-08-04","arxiv_id":"2008.01767","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuromorphic-computing-for-content-based","title":"Neuromorphic Computing for Content-based Image Retrieval","date":"2020-08-04","arxiv_id":"2008.01380","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-bayesian-bandits-exploring-in-online","title":"Deep Bayesian Bandits: Exploring in Online Personalized Recommendations","date":"2020-08-03","arxiv_id":"2008.00727","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-clarifying-question","title":"An Empirical Study of Clarifying Question-Based Systems","date":"2020-08-01","arxiv_id":"2008.00279","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-document-similarity-for-content","title":"Contextual Document Similarity for Content-based Literature Recommender Systems","date":"2020-08-01","arxiv_id":"2008.00202","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-citation-proximity-vcp-empowering","title":"Virtual Citation Proximity (VCP): Empowering Document Recommender Systems by Learning a Hypothetical In-Text Citation-Proximity Metric for Uncited Documents","date":"2020-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-ranking-oriented-recommender-system","title":"Embedding Ranking-Oriented Recommender System Graphs","date":"2020-07-31","arxiv_id":"2007.16173","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-long-term-social-welfare-in","title":"Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach","date":"2020-07-31","arxiv_id":"2008.00104","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-context-aware-recommender-systems","title":"Evolving Context-Aware Recommender Systems With Users in Mind","date":"2020-07-30","arxiv_id":"2007.15409","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-local-experts-for-dynamic","title":"Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk","date":"2020-07-29","arxiv_id":"2007.15091","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-feature-generation-via-learning","title":"FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data","date":"2020-07-29","arxiv_id":"2007.14573","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-influences-in-recommendation-systems","title":"Social Influences in Recommendation Systems","date":"2020-07-29","arxiv_id":"2007.15104","repositories_listed":0,"syntology":null},{"url":null,"slug":"glimg-global-and-local-item-graphs-for-top-n","title":"GLIMG: Global and Local Item Graphs for Top-N Recommender Systems","date":"2020-07-28","arxiv_id":"2007.14018","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-size-reduction-using-frequency-based","title":"Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems","date":"2020-07-28","arxiv_id":"2007.14523","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-bigraph-neural-network-as","title":"Hierarchical BiGraph Neural Network as Recommendation Systems","date":"2020-07-27","arxiv_id":"2007.16000","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-unexpected-recommendations","title":"Latent Unexpected Recommendations","date":"2020-07-27","arxiv_id":"2007.13280","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommending-podcasts-for-cold-start-users","title":"Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste","date":"2020-07-27","arxiv_id":"2007.13287","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-recommender-systems-function-in-the-health","title":"Do recommender systems function in the health domain: a system review","date":"2020-07-26","arxiv_id":"2007.13058","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-data-splitting-strategies-for-the","title":"Exploring Data Splitting Strategies for the Evaluation of Recommendation Models","date":"2020-07-26","arxiv_id":"2007.13237","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-boosting-deep-neural-networks-for","title":"Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate","date":"2020-07-26","arxiv_id":"2007.13087","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-symbolic-reasoning-over-knowledge","title":"Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation","date":"2020-07-26","arxiv_id":"2007.13207","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-loop-and-bias-amplification-in","title":"Feedback Loop and Bias Amplification in Recommender Systems","date":"2020-07-25","arxiv_id":"2007.13019","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-multistakeholder-impact-of","title":"Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration","date":"2020-07-23","arxiv_id":"2007.12230","repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-recommender-system-predicting","title":"Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles","date":"2020-07-23","arxiv_id":"2007.12161","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-to-or-not-to-trust-intelligent-machines","title":"When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games","date":"2020-07-22","arxiv_id":"2007.11338","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-vitae-recommendation-based-on-text","title":"Curriculum Vitae Recommendation Based on Text Mining","date":"2020-07-21","arxiv_id":"2007.11053","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-collaborative-filtering-models-for","title":"Hybrid Collaborative Filtering Models for Clinical Search Recommendation","date":"2020-07-19","arxiv_id":"2008.01193","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-level-rating-system-using-customer","title":"Feature-level Rating System using Customer Reviews and Review Votes","date":"2020-07-18","arxiv_id":"2007.09513","repositories_listed":0,"syntology":null},{"url":null,"slug":"prioritized-multi-criteria-federated-learning","title":"Prioritized Multi-Criteria Federated Learning","date":"2020-07-17","arxiv_id":"2007.08893","repositories_listed":0,"syntology":null},{"url":null,"slug":"reciprocal-recommender-systems-analysis-of","title":"Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation","date":"2020-07-17","arxiv_id":"2007.16120","repositories_listed":0,"syntology":null}],"record_sha256":"f6bb55f57e55bdbb1fbac72b6969fda2c1e666a4a457bb53805fe4c717bbe74b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}