{"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/collaborative-filtering/papers/8","list_of":"/task/collaborative-filtering","task":"Collaborative Filtering","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":8,"pages_in_order":14,"rows_per_page":100,"rows":[701,800],"of":1309,"counts":{"archive_papers_tagged":1309,"with_a_code_link":461,"where_syntology_ran_a_sample":63,"not_listed_spam_title":0,"listed":1309,"listed_where_code_ran":63,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":59,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":59,"listed_every_run_a_failure_of_syntologys_instrument":4,"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/collaborative-filtering","prev":"/task/collaborative-filtering/papers/7","next":"/task/collaborative-filtering/papers/9","papers":[{"url":null,"slug":"conversion-based-dynamic-creative","title":"Conversion-Based Dynamic-Creative-Optimization in Native Advertising","date":"2022-11-13","arxiv_id":"2211.11524","repositories_listed":0,"syntology":null},{"url":null,"slug":"timekit-a-time-series-forecasting-based","title":"TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering","date":"2022-11-08","arxiv_id":"2211.04266","repositories_listed":0,"syntology":null},{"url":null,"slug":"location-aware-web-service-qos-prediction-via","title":"Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering","date":"2022-11-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fusiondeepmf-a-dual-embedding-based-deep","title":"FusionDeepMF: A Dual Embedding based Deep Fusion Model for Recommendation","date":"2022-10-11","arxiv_id":"2210.05338","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-user-reinforcement-learning-with-low","title":"Multi-User Reinforcement Learning with Low Rank Rewards","date":"2022-10-11","arxiv_id":"2210.05355","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-personalized-product","title":"Multi-Objective Personalized Product Retrieval in Taobao Search","date":"2022-10-09","arxiv_id":"2210.04170","repositories_listed":0,"syntology":null},{"url":null,"slug":"scientific-and-technological-news","title":"Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception","date":"2022-10-07","arxiv_id":"2210.03295","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-graph-based-recommender-system-with","title":"Efficient Graph based Recommender System with Weighted Averaging of Messages","date":"2022-09-30","arxiv_id":"2209.15238","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-meters-integration-in-distribution","title":"Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process","date":"2022-09-30","arxiv_id":"2209.15239","repositories_listed":0,"syntology":null},{"url":null,"slug":"discussion-about-attacks-and-defenses-for","title":"Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design","date":"2022-09-28","arxiv_id":"2210.07817","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-beam-search-for-initial-access","title":"Efficient Beam Search for Initial Access Using Collaborative Filtering","date":"2022-09-14","arxiv_id":"2209.06669","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-challenge-based-survey-of-e-recruitment","title":"A challenge-based survey of e-recruitment recommendation systems","date":"2022-09-12","arxiv_id":"2209.05112","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-matrix-factorisation-for-large-scale","title":"Fair Matrix Factorisation for Large-Scale Recommender Systems","date":"2022-09-09","arxiv_id":"2209.04394","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-rec-surrounding-position-enhanced","title":"SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation","date":"2022-09-09","arxiv_id":"2209.04154","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-low-rank-matrix-completion","title":"Online Low Rank Matrix Completion","date":"2022-09-08","arxiv_id":"2209.03997","repositories_listed":0,"syntology":null},{"url":null,"slug":"tag-aware-document-representation-for","title":"Tag-Aware Document Representation for Research Paper Recommendation","date":"2022-09-08","arxiv_id":"2209.03660","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-scalable-recommendation-engine-for-new","title":"A Scalable Recommendation Engine for New Users and Items","date":"2022-09-06","arxiv_id":"2209.06128","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-recommendation-system-based-on-mind-1","title":"User recommendation system based on MIND dataset","date":"2022-09-06","arxiv_id":"2209.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-relations-between-sectors","title":"Extracting Relations Between Sectors","date":"2022-08-30","arxiv_id":"2208.14332","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-collaborative-filtering-thompson","title":"Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation","date":"2022-08-25","arxiv_id":"2208.11926","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-item-promotion-in-gnn-based","title":"Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective","date":"2022-08-21","arxiv_id":"2208.09979","repositories_listed":0,"syntology":null},{"url":null,"slug":"hysage-a-hybrid-static-and-adaptive-graph","title":"HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations","date":"2022-08-20","arxiv_id":"2208.09586","repositories_listed":0,"syntology":null},{"url":null,"slug":"lfgcf-light-folksonomy-graph-collaborative","title":"LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation","date":"2022-08-06","arxiv_id":"2208.03454","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-interaction-augmented-graph","title":"Geometric Interaction Augmented Graph Collaborative Filtering","date":"2022-08-02","arxiv_id":"2208.01250","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-sparsity-promoting-regularizers","title":"Learning Sparsity-Promoting Regularizers using Bilevel Optimization","date":"2022-07-18","arxiv_id":"2207.08939","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-moods-recommending-music-by-moods-on","title":"Flow Moods: Recommending Music by Moods on Deezer","date":"2022-07-15","arxiv_id":"2207.11229","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-relationship-between-counterfactual","title":"On the Relationship Between Counterfactual Explainer and Recommender","date":"2022-07-09","arxiv_id":"2207.04317","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-recommendations-for-optimal","title":"Interactive Recommendations for Optimal Allocations in Markets with Constraints","date":"2022-07-08","arxiv_id":"2207.04143","repositories_listed":0,"syntology":null},{"url":null,"slug":"item-recommendation-using-user-feedback-data","title":"Item Recommendation Using User Feedback Data and Item Profile","date":"2022-06-28","arxiv_id":"2206.14133","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-matrix-decomposition-model-based-on-feature","title":"A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System","date":"2022-06-12","arxiv_id":"2206.05654","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-modern-recommendation-system","title":"Contemporary Recommendation Systems on Big Data and Their Applications: A Survey","date":"2022-05-31","arxiv_id":"2206.02631","repositories_listed":0,"syntology":null},{"url":null,"slug":"hcfrec-hash-collaborative-filtering-via","title":"HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation","date":"2022-05-24","arxiv_id":"2205.12042","repositories_listed":0,"syntology":null},{"url":null,"slug":"reciperec-a-heterogeneous-graph-learning","title":"RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation","date":"2022-05-24","arxiv_id":"2205.14005","repositories_listed":0,"syntology":null},{"url":null,"slug":"pas-a-position-aware-similarity-measurement","title":"PAS: A Position-Aware Similarity Measurement for Sequential Recommendation","date":"2022-05-14","arxiv_id":"2205.06997","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedsplit-one-shot-federated-recommendation","title":"FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation","date":"2022-05-04","arxiv_id":"2205.02359","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-on-pushing-the-limits-of-baseline","title":"A Review on Pushing the Limits of Baseline Recommendation Systems with the integration of Opinion Mining & Information Retrieval Techniques","date":"2022-05-03","arxiv_id":"2205.01802","repositories_listed":0,"syntology":null},{"url":null,"slug":"explain-and-conquer-personalised-text-based","title":"Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency","date":"2022-05-03","arxiv_id":"2205.01759","repositories_listed":0,"syntology":null},{"url":null,"slug":"moviemat-context-aware-movie-recommendation","title":"MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting","date":"2022-04-27","arxiv_id":"2204.13003","repositories_listed":0,"syntology":null},{"url":null,"slug":"trading-hard-negatives-and-true-negatives-a","title":"Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach","date":"2022-04-25","arxiv_id":"2204.11752","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-empowered-content-aware","title":"Transformer-Empowered Content-Aware Collaborative Filtering","date":"2022-04-02","arxiv_id":"2204.00849","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-neighborhood-based-link-prediction","title":"Revisiting Neighborhood-based Link Prediction for Collaborative Filtering","date":"2022-03-29","arxiv_id":"2203.15789","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-of-codebook-latent-factors-for-cross","title":"Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data","date":"2022-03-26","arxiv_id":"2203.13995","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-recommender-systems-forget-learning","title":"Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation","date":"2022-03-22","arxiv_id":"2203.11491","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-expanding-graphs-for-signal","title":"Learning Expanding Graphs for Signal Interpolation","date":"2022-03-15","arxiv_id":"2203.07966","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-learning-of-the-inputs-and","title":"Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering","date":"2022-03-14","arxiv_id":"2203.07463","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-hybrid-active-learning-strategy","title":"An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering","date":"2022-03-11","arxiv_id":"2203.05954","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommendations-in-a-multi-domain-setting","title":"Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance","date":"2022-03-02","arxiv_id":"2203.01256","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-n-recommendation-algorithms-a-quest-for","title":"Top-N Recommendation Algorithms: A Quest for the State-of-the-Art","date":"2022-03-02","arxiv_id":"2203.01155","repositories_listed":0,"syntology":null},{"url":null,"slug":"wslrec-weakly-supervised-learning-for-neural","title":"WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models","date":"2022-02-28","arxiv_id":"2202.13616","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tech-hybrid-recommendation-engine-and","title":"A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)","date":"2022-02-13","arxiv_id":"2202.06248","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-filtering-with-attribution","title":"Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation","date":"2022-02-10","arxiv_id":"2202.04920","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-filtering-via-heterogeneous","title":"Collaborative filtering via heterogeneous neural networks","date":"2022-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-collaborative-filtering-bandits-via","title":"Neural Collaborative Filtering Bandits via Meta Learning","date":"2022-01-31","arxiv_id":"2201.13395","repositories_listed":0,"syntology":null},{"url":null,"slug":"coordinated-attacks-against-contextual","title":"Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms","date":"2022-01-30","arxiv_id":"2201.12700","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparsity-regularization-for-cold-start","title":"Sparsity Regularization For Cold-Start Recommendation","date":"2022-01-26","arxiv_id":"2201.10711","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-based-waveform-recommendation","title":"Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm","date":"2022-01-24","arxiv_id":"2202.01926","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-aspect-co-attentional-collaborative","title":"Multi-Aspect co-Attentional Collaborative Filtering for Extreme Multi-label Text Classification","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-contrastive-learning-for-2","title":"Supervised Contrastive Learning for Recommendation","date":"2022-01-10","arxiv_id":"2201.03144","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-recommendation-on-graphs","title":"Attention-Based Recommendation On Graphs","date":"2022-01-04","arxiv_id":"2201.05499","repositories_listed":0,"syntology":null},{"url":null,"slug":"pedagogical-word-recommendation-a-novel-task","title":"Pedagogical Word Recommendation: A novel task and dataset on personalized vocabulary acquisition for L2 learners","date":"2021-12-27","arxiv_id":"2112.13808","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-movie-recommendation-system","title":"Comprehensive Movie Recommendation System","date":"2021-12-23","arxiv_id":"2112.12463","repositories_listed":0,"syntology":null},{"url":null,"slug":"movie-recommender-system-using-critic","title":"Movie Recommender System using critic consensus","date":"2021-12-22","arxiv_id":"2112.11854","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-graph-pre-training-framework-for","title":"An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering","date":"2021-12-14","arxiv_id":"2112.07191","repositories_listed":0,"syntology":null},{"url":null,"slug":"cold-item-integration-in-deep-hybrid","title":"Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates","date":"2021-12-12","arxiv_id":"2112.07615","repositories_listed":0,"syntology":null},{"url":null,"slug":"combinations-of-jaccard-with-numerical","title":"Combinations of Jaccard with Numerical Measures for Collaborative Filtering Enhancement: Current Work and Future Proposal","date":"2021-11-24","arxiv_id":"2111.12202","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-deep-neural-networks-for","title":"Evolving Deep Neural Networks for Collaborative Filtering","date":"2021-11-15","arxiv_id":"2111.07758","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-divergence-of-latent-factors-via","title":"Mitigating Divergence of Latent Factors via Dual Ascent for Low Latency Event Prediction Models","date":"2021-11-15","arxiv_id":"2111.07866","repositories_listed":0,"syntology":null},{"url":null,"slug":"amazon-sagemaker-model-parallelism-a-general","title":"Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training","date":"2021-11-10","arxiv_id":"2111.05972","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-filtering-enriched-gnn-framework-for","title":"Content Filtering Enriched GNN Framework for News Recommendation","date":"2021-10-25","arxiv_id":"2110.12681","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-neural-vs-matrix-factorization","title":"Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives","date":"2021-10-23","arxiv_id":"2110.12141","repositories_listed":0,"syntology":null},{"url":null,"slug":"controllable-recommenders-using-deep","title":"Controllable Recommenders using Deep Generative Models and Disentanglement","date":"2021-10-11","arxiv_id":"2110.05056","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-bit-matrix-completion-with-differential","title":"One-Bit Matrix Completion with Differential Privacy","date":"2021-10-02","arxiv_id":"2110.00719","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-learning-algorithm-for-non","title":"A Multi-Task Learning Algorithm for Non-personalized Recommendations","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-user-item-similarity-in-hybrid","title":"Incorporating User-Item Similarity in Hybrid Neighborhood-based Recommendation System","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neuron-enhanced-autoencoder-based","title":"Neuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-attentive-social-temporal","title":"Extracting Attentive Social Temporal Excitation for Sequential Recommendation","date":"2021-09-28","arxiv_id":"2109.13539","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-hybrid-recommender-system","title":"An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods","date":"2021-09-12","arxiv_id":"2109.05516","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":"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":"when-product-search-meets-collaborative","title":"When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach","date":"2021-08-17","arxiv_id":"2108.07574","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":"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":"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":"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":"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":"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":"/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":"details-preserving-deep-collaborative","title":"Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising","date":"2021-07-11","arxiv_id":"2107.05115","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":null,"slug":"collaborative-filtering-based-method-for-low","title":"Collaborative Filtering-Based Method for Low-Resolution and Details Preserving Image Denoising","date":"2021-07-10","arxiv_id":"2107.04865","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":"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":"a-novel-method-for-recommendation-systems","title":"A novel method for recommendation systems using invasive weed optimization","date":"2021-06-05","arxiv_id":"2106.02831","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-neural-collaborative-filtering","title":"Federated Neural Collaborative Filtering","date":"2021-06-02","arxiv_id":"2106.04405","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-exercise-and-knowledge-aware","title":"Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction","date":"2021-06-01","arxiv_id":"2106.00263","repositories_listed":0,"syntology":null},{"url":null,"slug":"privileged-graph-distillation-for-cold-start","title":"Privileged Graph Distillation for Cold Start Recommendation","date":"2021-05-31","arxiv_id":"2105.14975","repositories_listed":0,"syntology":null},{"url":null,"slug":"causcf-causal-collaborative-filtering-for","title":"CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation","date":"2021-05-28","arxiv_id":"2105.13881","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-recommender-system-for-recommending","title":"A Hybrid Recommender System for Recommending Smartphones to Prospective Customers","date":"2021-05-26","arxiv_id":"2105.12876","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-movie-recommender-system-based-on","title":"Hybrid Movie Recommender System based on Resource Allocation","date":"2021-05-25","arxiv_id":"2105.11678","repositories_listed":0,"syntology":null}],"record_sha256":"cd99b755520ae366b83853c68c5fb3faf8edb4cd96a0acc7283972f77f0913f9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}