{"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/36","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":36,"pages_in_order":61,"rows_per_page":100,"rows":[3501,3600],"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/35","next":"/task/recommendation-systems/papers/37","papers":[{"url":null,"slug":"bayesian-knowledge-driven-critiquing-with","title":"Bayesian Knowledge-driven Critiquing with Indirect Evidence","date":"2023-06-09","arxiv_id":"2306.05636","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-explanation-with-varying-level-of","title":"Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System","date":"2023-06-09","arxiv_id":"2306.05809","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-knowledge-enhancement-for-zero","title":"Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application","date":"2023-06-09","arxiv_id":"2306.06302","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-gap-based-deterministic-tensor","title":"Spectral gap-based deterministic tensor completion","date":"2023-06-09","arxiv_id":"2306.06262","repositories_listed":0,"syntology":null},{"url":"/paper/answering-compositional-queries-with-set","slug":"answering-compositional-queries-with-set","title":"Answering Compositional Queries with Set-Theoretic Embeddings","date":"2023-06-07","arxiv_id":"2306.04133","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraint-based-recommender-system-for","title":"Constraint-based recommender system for crisis management simulations","date":"2023-06-07","arxiv_id":"2306.04553","repositories_listed":0,"syntology":null},{"url":null,"slug":"embracing-uncertainty-adaptive-vague","title":"Vague Preference Policy Learning for Conversational Recommendation","date":"2023-06-07","arxiv_id":"2306.04487","repositories_listed":0,"syntology":null},{"url":null,"slug":"pane-gnn-unifying-positive-and-negative-edges","title":"PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation","date":"2023-06-07","arxiv_id":"2306.04095","repositories_listed":0,"syntology":null},{"url":null,"slug":"set-to-sequence-ranking-based-concept-aware","title":"Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation","date":"2023-06-07","arxiv_id":"2306.04234","repositories_listed":0,"syntology":null},{"url":null,"slug":"xinsight-revealing-model-insights-for-gnns","title":"XInsight: Revealing Model Insights for GNNs with Flow-based Explanations","date":"2023-06-07","arxiv_id":"2306.04791","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-based-progressive-regression-model-for","title":"Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation","date":"2023-06-06","arxiv_id":"2306.03392","repositories_listed":0,"syntology":null},{"url":null,"slug":"construction-d-un-systeme-de-recommandation","title":"Construction d'un système de recommandation basé sur des contraintes via des graphes de connaissances","date":"2023-06-05","arxiv_id":"2306.03247","repositories_listed":0,"syntology":null},{"url":null,"slug":"ctrl-connect-tabular-and-language-model-for","title":"CTRL: Connect Collaborative and Language Model for CTR Prediction","date":"2023-06-05","arxiv_id":"2306.02841","repositories_listed":0,"syntology":null},{"url":null,"slug":"gen-ir-sigir-2023-the-first-workshop-on","title":"Gen-IR @ SIGIR 2023: The First Workshop on Generative Information Retrieval","date":"2023-06-05","arxiv_id":"2306.02887","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-federated-domain-adaptation-for","title":"Personalized Federated Domain Adaptation for Item-to-Item Recommendation","date":"2023-06-05","arxiv_id":"2306.03191","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-explainability-of-graph-neural","title":"A Survey on Explainability of Graph Neural Networks","date":"2023-06-02","arxiv_id":"2306.01958","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-reinforcement-learning-for-8","title":"Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems","date":"2023-06-02","arxiv_id":"2306.01476","repositories_listed":0,"syntology":null},{"url":null,"slug":"study-socially-aware-temporally-casual","title":"STUDY: Socially Aware Temporally Causal Decoder Recommender Systems","date":"2023-06-02","arxiv_id":"2306.07946","repositories_listed":0,"syntology":null},{"url":null,"slug":"systeme-de-recommandations-base-sur-les","title":"Système de recommandations basé sur les contraintes pour les simulations de gestion de crise","date":"2023-06-02","arxiv_id":"2306.01504","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-modal-latent-features-based-service","title":"A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things","date":"2023-06-01","arxiv_id":"2306.01163","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-fairness-aware-recommender","title":"A Survey on Fairness-aware Recommender Systems","date":"2023-06-01","arxiv_id":"2306.00403","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-community-detection-via","title":"Semi-supervised Community Detection via Structural Similarity Metrics","date":"2023-06-01","arxiv_id":"2306.01089","repositories_listed":0,"syntology":null},{"url":null,"slug":"representer-point-selection-for-explaining-1","title":"Representer Point Selection for Explaining Regularized High-dimensional Models","date":"2023-05-31","arxiv_id":"2305.20002","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-news-delivery-channel-recommendation","title":"The News Delivery Channel Recommendation Based on Granular Neural Network","date":"2023-05-30","arxiv_id":"2306.10022","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-exploration-matters-improving-both","title":"Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender","date":"2023-05-29","arxiv_id":"2306.00009","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-body-shape-classification-based-on-a","title":"Human Body Shape Classification Based on a Single Image","date":"2023-05-29","arxiv_id":"2305.18480","repositories_listed":0,"syntology":null},{"url":null,"slug":"choosing-the-right-weights-balancing-value","title":"Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems","date":"2023-05-27","arxiv_id":"2305.17428","repositories_listed":0,"syntology":null},{"url":null,"slug":"caramel-a-succinct-read-only-lookup-table-via","title":"CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions","date":"2023-05-26","arxiv_id":"2305.16545","repositories_listed":0,"syntology":null},{"url":null,"slug":"fara-future-aware-ranking-algorithm-for","title":"FARA: Future-aware Ranking Algorithm for Fairness Optimization","date":"2023-05-26","arxiv_id":"2305.16637","repositories_listed":0,"syntology":null},{"url":null,"slug":"justification-vs-transparency-why-and-how","title":"Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System","date":"2023-05-26","arxiv_id":"2305.17034","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-exploitation-bias-in-learning-to","title":"Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach","date":"2023-05-26","arxiv_id":"2305.16606","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-document-embeddings-via-self","title":"Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning","date":"2023-05-25","arxiv_id":"2305.16031","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-model-agnostic-data-subsampling","title":"Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems","date":"2023-05-25","arxiv_id":"2305.16391","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-influence-functions-classification","title":"On Influence Functions, Classification Influence, Relative Influence, Memorization and Generalization","date":"2023-05-25","arxiv_id":"2305.16094","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-for-user-interest","title":"Large Language Models for User Interest Journeys","date":"2023-05-24","arxiv_id":"2305.15498","repositories_listed":0,"syntology":null},{"url":null,"slug":"representation-online-matters-practical-end","title":"Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems","date":"2023-05-24","arxiv_id":"2305.15534","repositories_listed":0,"syntology":null},{"url":null,"slug":"advances-and-challenges-of-multi-task","title":"Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey","date":"2023-05-23","arxiv_id":"2305.13843","repositories_listed":0,"syntology":null},{"url":null,"slug":"conversational-recommendation-as-retrieval-a","title":"Conversational Recommendation as Retrieval: A Simple, Strong Baseline","date":"2023-05-23","arxiv_id":"2305.13725","repositories_listed":0,"syntology":null},{"url":null,"slug":"curse-of-low-dimensionality-in-recommender","title":"Curse of \"Low\" Dimensionality in Recommender Systems","date":"2023-05-23","arxiv_id":"2305.13597","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-long-term-value-for-auction-based","title":"Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning","date":"2023-05-23","arxiv_id":"2305.13747","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-news-recommendation-ecosystem-for","title":"Simulating News Recommendation Ecosystem for Fun and Profit","date":"2023-05-23","arxiv_id":"2305.14103","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-evolution-of-distributed-systems-for","title":"The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey","date":"2023-05-23","arxiv_id":"2305.13854","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-large-scale-vision-representation","title":"Efficient Large-Scale Visual Representation Learning And Evaluation","date":"2023-05-22","arxiv_id":"2305.13399","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-and-exploiting-data-heterogeneity","title":"Exploring and Exploiting Data Heterogeneity in Recommendation","date":"2023-05-21","arxiv_id":"2305.15431","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-channel-integrated-recommendation-with","title":"Multi-channel Integrated Recommendation with Exposure Constraints","date":"2023-05-21","arxiv_id":"2305.12319","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-factor-sequential-re-ranking-with","title":"Multi-factor Sequential Re-ranking with Perception-Aware Diversification","date":"2023-05-21","arxiv_id":"2305.12420","repositories_listed":0,"syntology":null},{"url":null,"slug":"dadin-domain-adversarial-deep-interest","title":"DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems","date":"2023-05-20","arxiv_id":"2305.12058","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-upper-limits-of-text-based","title":"Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights","date":"2023-05-19","arxiv_id":"2305.11700","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-in-a-creator-economy","title":"Online Learning in a Creator Economy","date":"2023-05-19","arxiv_id":"2305.11381","repositories_listed":0,"syntology":null},{"url":null,"slug":"visualization-for-recommendation","title":"Visualization for Recommendation Explainability: A Survey and New Perspectives","date":"2023-05-19","arxiv_id":"2305.11755","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-recommendation-system-serendipity","title":"Improving Recommendation System Serendipity Through Lexicase Selection","date":"2023-05-18","arxiv_id":"2305.11044","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-item-relevance-in-training-loss","title":"Integrating Item Relevance in Training Loss for Sequential Recommender Systems","date":"2023-05-18","arxiv_id":"2305.10824","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-recommendation-system-for","title":"Machine Learning Recommendation System For Health Insurance Decision Making In Nigeria","date":"2023-05-18","arxiv_id":"2305.10708","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-completion-models-are-few","title":"Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs","date":"2023-05-17","arxiv_id":"2305.09858","repositories_listed":0,"syntology":null},{"url":null,"slug":"consumer-side-fairness-in-recommender-systems","title":"Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation","date":"2023-05-16","arxiv_id":"2305.09330","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-diversity-in-information-access","title":"Fairness and Diversity in Information Access Systems","date":"2023-05-16","arxiv_id":"2305.09319","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-in-context-learning-capabilities-of","title":"Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text","date":"2023-05-15","arxiv_id":"2305.08804","repositories_listed":0,"syntology":null},{"url":null,"slug":"manipulating-visually-aware-federated","title":"Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures","date":"2023-05-14","arxiv_id":"2305.08183","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-interactive-collaborative","title":"Multi-View Interactive Collaborative Filtering","date":"2023-05-14","arxiv_id":"2305.18306","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-large-language-models-in","title":"Leveraging Large Language Models in Conversational Recommender Systems","date":"2023-05-13","arxiv_id":"2305.07961","repositories_listed":0,"syntology":null},{"url":null,"slug":"dish-detection-in-food-platters-a-framework","title":"Dish detection in food platters: A framework for automated diet logging and nutrition management","date":"2023-05-12","arxiv_id":"2305.07552","repositories_listed":0,"syntology":null},{"url":null,"slug":"eye-tracking-as-a-source-of-implicit-feedback","title":"Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis","date":"2023-05-12","arxiv_id":"2305.07516","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-accuracy-and-low-regret-for-user-cold","title":"High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits","date":"2023-05-12","arxiv_id":"2305.18305","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-soft-integration-for-multimodal","title":"Knowledge Soft Integration for Multimodal Recommendation","date":"2023-05-12","arxiv_id":"2305.07419","repositories_listed":0,"syntology":null},{"url":null,"slug":"mem-rec-memory-efficient-recommendation","title":"Mem-Rec: Memory Efficient Recommendation System using Alternative Representation","date":"2023-05-12","arxiv_id":"2305.07205","repositories_listed":0,"syntology":null},{"url":null,"slug":"palr-personalization-aware-llms-for","title":"PALR: Personalization Aware LLMs for Recommendation","date":"2023-05-12","arxiv_id":"2305.07622","repositories_listed":0,"syntology":null},{"url":null,"slug":"value-of-exploration-measurements-findings","title":"Long-Term Value of Exploration: Measurements, Findings and Algorithms","date":"2023-05-12","arxiv_id":"2305.07764","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-item-based-recommendation-via-multi","title":"Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training","date":"2023-05-12","arxiv_id":"2305.07633","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-data-denoising-for-recommendation","title":"Automated Data Denoising for Recommendation","date":"2023-05-11","arxiv_id":"2305.07070","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-life-stay-informed-around-you-a","title":"Local Life: Stay Informed Around You, A Scalable Geoparsing and Geotagging Approach to Serve Local News Worldwide","date":"2023-05-11","arxiv_id":"2305.07168","repositories_listed":0,"syntology":null},{"url":null,"slug":"perfedrec-enhancing-personalized-federated","title":"PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training","date":"2023-05-11","arxiv_id":"2305.06622","repositories_listed":0,"syntology":null},{"url":null,"slug":"ppgencdr-a-stable-and-robust-framework-for","title":"PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation","date":"2023-05-11","arxiv_id":"2305.16163","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommendation-as-instruction-following-a","title":"Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach","date":"2023-05-11","arxiv_id":"2305.07001","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-landscape-of-machine-unlearning","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","date":"2023-05-10","arxiv_id":"2305.06360","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-recommender-systems-with","title":"Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models","date":"2023-05-10","arxiv_id":"2305.05973","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-recommender-with-geometric","title":"Explainable Recommender with Geometric Information Bottleneck","date":"2023-05-09","arxiv_id":"2305.05331","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-of-federated-learning-models-for","title":"Survey of Federated Learning Models for Spatial-Temporal Mobility Applications","date":"2023-05-09","arxiv_id":"2305.05257","repositories_listed":0,"syntology":null},{"url":null,"slug":"turning-privacy-preserving-mechanisms-against","title":"Turning Privacy-preserving Mechanisms against Federated Learning","date":"2023-05-09","arxiv_id":"2305.05355","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-impact-of-user-cluster-targeted","title":"Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders","date":"2023-05-08","arxiv_id":"2305.04694","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-high-dimensional-and-low-rank-tensor","title":"On High-dimensional and Low-rank Tensor Bandits","date":"2023-05-06","arxiv_id":"2305.03884","repositories_listed":0,"syntology":null},{"url":null,"slug":"sincere-sequential-interaction-networks","title":"SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds","date":"2023-05-06","arxiv_id":"2305.03883","repositories_listed":0,"syntology":null},{"url":null,"slug":"retraining-a-graph-based-recommender-with","title":"Retraining A Graph-based Recommender with Interests Disentanglement","date":"2023-05-05","arxiv_id":"2305.03624","repositories_listed":0,"syntology":null},{"url":null,"slug":"u-need-a-fine-grained-dataset-for-user-needs","title":"U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation","date":"2023-05-05","arxiv_id":"2305.04774","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-application-of-affective-measures-in-text","title":"The Application of Affective Measures in Text-based Emotion Aware Recommender Systems","date":"2023-05-04","arxiv_id":"2305.04796","repositories_listed":0,"syntology":null},{"url":null,"slug":"weighted-tallying-bandits-overcoming","title":"Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality","date":"2023-05-04","arxiv_id":"2305.02955","repositories_listed":0,"syntology":null},{"url":null,"slug":"citecaselaw-citation-worthiness-detection-in","title":"CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing","date":"2023-05-03","arxiv_id":"2305.03508","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-multi-modal-sequential-recommenders","title":"Denoising Multi-modal Sequential Recommenders with Contrastive Learning","date":"2023-05-03","arxiv_id":"2305.01915","repositories_listed":0,"syntology":null},{"url":null,"slug":"ripple-knowledge-graph-convolutional-networks","title":"Ripple Knowledge Graph Convolutional Networks For Recommendation Systems","date":"2023-05-02","arxiv_id":"2305.01147","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-aware-incremental-learning-with","title":"Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems","date":"2023-05-02","arxiv_id":"2305.01204","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-newer-is-not-better-does-deep-learning","title":"When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?","date":"2023-05-02","arxiv_id":"2305.01801","repositories_listed":0,"syntology":null},{"url":null,"slug":"explicit-knowledge-graph-reasoning-for","title":"Explicit Knowledge Graph Reasoning for Conversational Recommendation","date":"2023-05-01","arxiv_id":"2305.00783","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-response-interpretation-for","title":"Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research","date":"2023-04-30","arxiv_id":"2305.00577","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-dark-side-of-explanations-poisoning","title":"The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples","date":"2023-04-30","arxiv_id":"2305.00574","repositories_listed":0,"syntology":null},{"url":null,"slug":"systematic-review-on-reinforcement-learning","title":"Systematic Review on Reinforcement Learning in the Field of Fintech","date":"2023-04-29","arxiv_id":"2305.07466","repositories_listed":0,"syntology":null},{"url":null,"slug":"mcprioq-a-lock-free-algorithm-for-online","title":"MCPrioQ: A lock-free algorithm for online sparse markov-chains","date":"2023-04-28","arxiv_id":"2304.14801","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-best-of-both-worlds-algorithm-for","title":"A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints","date":"2023-04-27","arxiv_id":"2304.14326","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-impact-of-culture-in","title":"Understanding the Impact of Culture in Assessing Helpfulness of Online Reviews","date":"2023-04-27","arxiv_id":"2305.04836","repositories_listed":0,"syntology":null},{"url":null,"slug":"improvements-on-recommender-system-based-on","title":"Improvements on Recommender System based on Mathematical Principles","date":"2023-04-26","arxiv_id":"2304.13579","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupa-an-industrial-recommender-system-for","title":"COUPA: An Industrial Recommender System for Online to Offline Service Platforms","date":"2023-04-25","arxiv_id":"2304.12549","repositories_listed":0,"syntology":null},{"url":null,"slug":"excalibr-expected-calibration-of","title":"ExCalibR: Expected Calibration of Recommendations","date":"2023-04-24","arxiv_id":"2304.12311","repositories_listed":0,"syntology":null}],"record_sha256":"d73e1186063f5c3f125d2d64b837697f5f96df4f7da1e1ea7ca5917869b22d23","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}