{"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/3","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":3,"pages_in_order":14,"rows_per_page":100,"rows":[201,300],"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/2","next":"/task/collaborative-filtering/papers/4","papers":[{"url":"/paper/on-generative-agents-in-recommendation","slug":"on-generative-agents-in-recommendation","title":"On Generative Agents in Recommendation","date":"2023-10-16","arxiv_id":"2310.10108","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-generative-agents-in-recommendation#ran","syntology_url":"https://syntology.ai/paper/2310.10108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10108"}},"official":{"repos":["LehengTHU/Agent4Rec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-collaborative-filtering-to-popularity","slug":"robust-collaborative-filtering-to-popularity","title":"Robust Collaborative Filtering to Popularity Distribution Shift","date":"2023-10-16","arxiv_id":"2310.10696","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-collaborative-filtering-to-popularity#ran","syntology_url":"https://syntology.ai/paper/2310.10696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10696"}},"official":{"repos":["anzhang314/popgo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scalable-approximate-nonsymmetric-autoencoder","slug":"scalable-approximate-nonsymmetric-autoencoder","title":"Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering","date":"2023-09-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/can-transformer-and-gnn-help-each-other","slug":"can-transformer-and-gnn-help-each-other","title":"TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems","date":"2023-08-28","arxiv_id":"2308.14355","repositories_listed":1,"syntology":null},{"url":"/paper/a-topology-aware-analysis-of-graph","slug":"a-topology-aware-analysis-of-graph","title":"A Topology-aware Analysis of Graph Collaborative Filtering","date":"2023-08-21","arxiv_id":"2308.10778","repositories_listed":1,"syntology":null},{"url":"/paper/graph-based-alignment-and-uniformity-for","slug":"graph-based-alignment-and-uniformity-for","title":"Graph-based Alignment and Uniformity for Recommendation","date":"2023-08-18","arxiv_id":"2308.09292","repositories_listed":1,"syntology":null},{"url":"/paper/autoseqrec-autoencoder-for-efficient","slug":"autoseqrec-autoencoder-for-efficient","title":"AutoSeqRec: Autoencoder for Efficient Sequential Recommendation","date":"2023-08-14","arxiv_id":"2308.06878","repositories_listed":1,"syntology":null},{"url":"/paper/augmented-negative-sampling-for-collaborative","slug":"augmented-negative-sampling-for-collaborative","title":"Augmented Negative Sampling for Collaborative Filtering","date":"2023-08-11","arxiv_id":"2308.05972","repositories_listed":1,"syntology":null},{"url":"/paper/topic-level-bayesian-surprise-and-serendipity","slug":"topic-level-bayesian-surprise-and-serendipity","title":"Topic-Level Bayesian Surprise and Serendipity for Recommender Systems","date":"2023-08-11","arxiv_id":"2308.06368","repositories_listed":1,"syntology":null},{"url":"/paper/toward-a-better-understanding-of-loss","slug":"toward-a-better-understanding-of-loss","title":"Toward a Better Understanding of Loss Functions for Collaborative Filtering","date":"2023-08-11","arxiv_id":"2308.06091","repositories_listed":1,"syntology":null},{"url":"/paper/sslrec-a-self-supervised-learning-library-for","slug":"sslrec-a-self-supervised-learning-library-for","title":"SSLRec: A Self-Supervised Learning Framework for Recommendation","date":"2023-08-10","arxiv_id":"2308.05697","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sslrec-a-self-supervised-learning-library-for#ran","syntology_url":"https://syntology.ai/paper/2308.05697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05697"}},"official":{"repos":["hkuds/sslrec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/online-distillation-enhanced-multi-modal","slug":"online-distillation-enhanced-multi-modal","title":"Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation","date":"2023-08-08","arxiv_id":"2308.04067","repositories_listed":1,"syntology":null},{"url":"/paper/adrnet-a-generalized-collaborative-filtering","slug":"adrnet-a-generalized-collaborative-filtering","title":"ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction","date":"2023-08-03","arxiv_id":"2308.02571","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-recklessness-to-collaborative","slug":"incorporating-recklessness-to-collaborative","title":"Incorporating Recklessness to Collaborative Filtering based Recommender Systems","date":"2023-08-03","arxiv_id":"2308.02058","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-aware-collaborative-filtering-with","slug":"knowledge-aware-collaborative-filtering-with","title":"Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction","date":"2023-08-02","arxiv_id":"2308.02555","repositories_listed":1,"syntology":null},{"url":"/paper/challenging-the-myth-of-graph-collaborative","slug":"challenging-the-myth-of-graph-collaborative","title":"Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis","date":"2023-08-01","arxiv_id":"2308.00404","repositories_listed":1,"syntology":null},{"url":"/paper/debiased-pairwise-learning-from-positive","slug":"debiased-pairwise-learning-from-positive","title":"Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback","date":"2023-07-29","arxiv_id":"2307.15973","repositories_listed":1,"syntology":null},{"url":"/paper/lightgt-a-light-graph-transformer-for","slug":"lightgt-a-light-graph-transformer-for","title":"LightGT: A Light Graph Transformer for Multimedia Recommendation","date":"2023-07-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-joint-hyperparameter-and","slug":"efficient-and-joint-hyperparameter-and","title":"Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering","date":"2023-07-12","arxiv_id":"2307.11004","repositories_listed":1,"syntology":null},{"url":"/paper/generative-contrastive-graph-learning-for","slug":"generative-contrastive-graph-learning-for","title":"Generative Contrastive Graph Learning for Recommendation","date":"2023-07-11","arxiv_id":"2307.05100","repositories_listed":1,"syntology":null},{"url":"/paper/causal-neural-graph-collaborative-filtering","slug":"causal-neural-graph-collaborative-filtering","title":"Neural Causal Graph Collaborative Filtering","date":"2023-07-10","arxiv_id":"2307.04384","repositories_listed":1,"syntology":null},{"url":"/paper/dimension-independent-mixup-for-hard-negative","slug":"dimension-independent-mixup-for-hard-negative","title":"Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering","date":"2023-06-28","arxiv_id":"2306.15905","repositories_listed":1,"syntology":null},{"url":"/paper/mean-variance-efficient-collaborative","slug":"mean-variance-efficient-collaborative","title":"Mean-Variance Efficient Collaborative Filtering for Stock Recommendation","date":"2023-06-11","arxiv_id":"2306.06590","repositories_listed":1,"syntology":null},{"url":"/paper/safe-collaborative-filtering","slug":"safe-collaborative-filtering","title":"Safe Collaborative Filtering","date":"2023-06-08","arxiv_id":"2306.05292","repositories_listed":1,"syntology":null},{"url":"/paper/on-manipulating-signals-of-user-item-graph-a","slug":"on-manipulating-signals-of-user-item-graph-a","title":"On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering","date":"2023-06-06","arxiv_id":"2306.03624","repositories_listed":1,"syntology":null},{"url":"/paper/graph-transformer-for-recommendation","slug":"graph-transformer-for-recommendation","title":"Graph Transformer for Recommendation","date":"2023-06-04","arxiv_id":"2306.02330","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-transformer-for-recommendation#ran","syntology_url":"https://syntology.ai/paper/2306.02330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02330"}},"official":{"repos":["hkuds/gformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pure-spectral-graph-embeddings-reinterpreting","slug":"pure-spectral-graph-embeddings-reinterpreting","title":"Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation","date":"2023-05-28","arxiv_id":"2305.18374","repositories_listed":1,"syntology":null},{"url":"/paper/uctrl-unbiased-contrastive-representation","slug":"uctrl-unbiased-contrastive-representation","title":"uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering","date":"2023-05-22","arxiv_id":"2305.12768","repositories_listed":1,"syntology":null},{"url":"/paper/popularity-debiasing-from-exposure-to","slug":"popularity-debiasing-from-exposure-to","title":"Popularity Debiasing from Exposure to Interaction in Collaborative Filtering","date":"2023-05-09","arxiv_id":"2305.05204","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-contrastive-collaborative","slug":"disentangled-contrastive-collaborative","title":"Disentangled Contrastive Collaborative Filtering","date":"2023-05-04","arxiv_id":"2305.02759","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/disentangled-contrastive-collaborative#ran","syntology_url":"https://syntology.ai/paper/2305.02759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02759"}},"official":{"repos":["hkuds/dccf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-explainable-collaborative-filtering","slug":"towards-explainable-collaborative-filtering","title":"Towards Explainable Collaborative Filtering with Taste Clusters Learning","date":"2023-04-27","arxiv_id":"2304.13937","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-residual-metric-learning","slug":"collaborative-residual-metric-learning","title":"Collaborative Residual Metric Learning","date":"2023-04-17","arxiv_id":"2304.07971","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/collaborative-residual-metric-learning#ran","syntology_url":"https://syntology.ai/paper/2304.07971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.07971"}},"official":{"repos":["Joinn99/CoRML"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/heat-a-highly-efficient-and-affordable","slug":"heat-a-highly-efficient-and-affordable","title":"HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs","date":"2023-04-14","arxiv_id":"2304.07334","repositories_listed":1,"syntology":null},{"url":"/paper/sheaf-neural-networks-for-graph-based","slug":"sheaf-neural-networks-for-graph-based","title":"Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems","date":"2023-04-07","arxiv_id":"2304.09097","repositories_listed":1,"syntology":null},{"url":"/paper/graph-collaborative-signals-denoising-and","slug":"graph-collaborative-signals-denoising-and","title":"Graph Collaborative Signals Denoising and Augmentation for Recommendation","date":"2023-04-06","arxiv_id":"2304.03344","repositories_listed":1,"syntology":null},{"url":"/paper/hgcc-enhancing-hyperbolic-graph-convolution","slug":"hgcc-enhancing-hyperbolic-graph-convolution","title":"HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation","date":"2023-04-06","arxiv_id":"2304.02961","repositories_listed":1,"syntology":null},{"url":"/paper/item-graph-convolution-collaborative","slug":"item-graph-convolution-collaborative","title":"Item Graph Convolution Collaborative Filtering for Inductive Recommendations","date":"2023-03-28","arxiv_id":"2303.15946","repositories_listed":1,"syntology":null},{"url":"/paper/graph-less-collaborative-filtering","slug":"graph-less-collaborative-filtering","title":"Graph-less Collaborative Filtering","date":"2023-03-15","arxiv_id":"2303.08537","repositories_listed":1,"syntology":null},{"url":"/paper/neural-group-recommendation-based-on-a","slug":"neural-group-recommendation-based-on-a","title":"Neural Group Recommendation Based on a Probabilistic Semantic Aggregation","date":"2023-03-13","arxiv_id":"2303.07001","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-privacy-preferences-for-smart","slug":"predicting-privacy-preferences-for-smart","title":"Predicting Privacy Preferences for Smart Devices as Norms","date":"2023-02-21","arxiv_id":"2302.10650","repositories_listed":1,"syntology":null},{"url":"/paper/improving-recommendation-fairness-via-data","slug":"improving-recommendation-fairness-via-data","title":"Improving Recommendation Fairness via Data Augmentation","date":"2023-02-13","arxiv_id":"2302.06333","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-and-prompt-tuning-for-multi","slug":"denoising-and-prompt-tuning-for-multi","title":"Denoising and Prompt-Tuning for Multi-Behavior Recommendation","date":"2023-02-12","arxiv_id":"2302.05862","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-collaborative-filtering-to","slug":"invariant-collaborative-filtering-to","title":"Invariant Collaborative Filtering to Popularity Distribution Shift","date":"2023-02-10","arxiv_id":"2302.05328","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/invariant-collaborative-filtering-to#ran","syntology_url":"https://syntology.ai/paper/2302.05328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05328"}},"official":{"repos":["anzhang314/invcf"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-lightweight-cross-domain-sequential","slug":"towards-lightweight-cross-domain-sequential","title":"Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network","date":"2023-02-07","arxiv_id":"2302.03221","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-collaborative-filtering-for-cold","slug":"contrastive-collaborative-filtering-for-cold","title":"Contrastive Collaborative Filtering for Cold-Start Item Recommendation","date":"2023-02-04","arxiv_id":"2302.02151","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-for-knowledge-graph-based","slug":"causal-inference-for-knowledge-graph-based","title":"Causal Inference for Knowledge Graph based Recommendation","date":"2022-12-20","arxiv_id":"2212.10046","repositories_listed":1,"syntology":null},{"url":"/paper/variational-factorization-machines-for","slug":"variational-factorization-machines-for","title":"Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems","date":"2022-12-20","arxiv_id":"2212.09920","repositories_listed":1,"syntology":null},{"url":"/paper/a-generalized-latent-factor-model-approach-to","slug":"a-generalized-latent-factor-model-approach-to","title":"A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency","date":"2022-11-17","arxiv_id":"2211.09272","repositories_listed":1,"syntology":null},{"url":"/paper/perturbation-recovery-method-for","slug":"perturbation-recovery-method-for","title":"Blurring-Sharpening Process Models for Collaborative Filtering","date":"2022-11-17","arxiv_id":"2211.09324","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/perturbation-recovery-method-for#ran","syntology_url":"https://syntology.ai/paper/2211.09324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09324"}},"official":{"repos":["jeongwhanchoi/bspm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/incorporating-bias-aware-margins-into","slug":"incorporating-bias-aware-margins-into","title":"Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering","date":"2022-10-20","arxiv_id":"2210.11054","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/incorporating-bias-aware-margins-into#ran","syntology_url":"https://syntology.ai/paper/2210.11054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11054"}},"official":{"repos":["anzhang314/bc-loss"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mdgcf-multi-dependency-graph-collaborative","slug":"mdgcf-multi-dependency-graph-collaborative","title":"MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies","date":"2022-10-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/megcf-multimodal-entity-graph-collaborative","slug":"megcf-multimodal-entity-graph-collaborative","title":"MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation","date":"2022-10-14","arxiv_id":"2210.07443","repositories_listed":1,"syntology":null},{"url":"/paper/sapling-similarity-outperforms-other-local","slug":"sapling-similarity-outperforms-other-local","title":"Sapling Similarity: a performing and interpretable memory-based tool for recommendation","date":"2022-10-13","arxiv_id":"2210.07039","repositories_listed":1,"syntology":null},{"url":"/paper/joint-multi-grained-popularity-aware-graph","slug":"joint-multi-grained-popularity-aware-graph","title":"Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation","date":"2022-10-10","arxiv_id":"2210.04614","repositories_listed":1,"syntology":null},{"url":"/paper/resbemf-improving-prediction-coverage-of","slug":"resbemf-improving-prediction-coverage-of","title":"Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering","date":"2022-10-05","arxiv_id":"2210.10619","repositories_listed":1,"syntology":null},{"url":"/paper/the-minority-matters-a-diversity-promoting","slug":"the-minority-matters-a-diversity-promoting","title":"The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm","date":"2022-09-30","arxiv_id":"2209.15292","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-contrastive-learning-enhanced","slug":"spatio-temporal-contrastive-learning-enhanced","title":"Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation","date":"2022-09-23","arxiv_id":"2209.11461","repositories_listed":1,"syntology":null},{"url":"/paper/the-effectiveness-of-factorization-and","slug":"the-effectiveness-of-factorization-and","title":"The effectiveness of factorization and similarity blending","date":"2022-09-16","arxiv_id":"2209.13011","repositories_listed":1,"syntology":null},{"url":"/paper/causal-inference-in-recommender-systems-a","slug":"causal-inference-in-recommender-systems-a","title":"Causal Inference in Recommender Systems: A Survey and Future Directions","date":"2022-08-26","arxiv_id":"2208.12397","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-sgd-for-highly-ill-conditioned","slug":"accelerating-sgd-for-highly-ill-conditioned","title":"Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion","date":"2022-08-24","arxiv_id":"2208.11246","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-causal-collaborative-filtering","slug":"dynamic-causal-collaborative-filtering","title":"Dynamic Causal Collaborative Filtering","date":"2022-08-23","arxiv_id":"2208.11094","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-hypergraph-transformer-for","slug":"self-supervised-hypergraph-transformer-for","title":"Self-Supervised Hypergraph Transformer for Recommender Systems","date":"2022-07-28","arxiv_id":"2207.14338","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-collaborative-filtering-recommender","slug":"enhancing-collaborative-filtering-recommender","title":"Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis","date":"2022-07-19","arxiv_id":"2207.12883","repositories_listed":1,"syntology":null},{"url":"/paper/hicf-hyperbolic-informative-collaborative","slug":"hicf-hyperbolic-informative-collaborative","title":"HICF: Hyperbolic Informative Collaborative Filtering","date":"2022-07-19","arxiv_id":"2207.09051","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-scalable-recommendation-via","slug":"efficient-and-scalable-recommendation-via","title":"Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation","date":"2022-07-13","arxiv_id":"2207.05959","repositories_listed":1,"syntology":null},{"url":"/paper/modelling-users-with-item-metadata-for","slug":"modelling-users-with-item-metadata-for","title":"Modelling Users with Item Metadata for Explainable and Interactive Recommendation","date":"2022-07-01","arxiv_id":"2207.00350","repositories_listed":1,"syntology":null},{"url":"/paper/unlearning-protected-user-attributes-in","slug":"unlearning-protected-user-attributes-in","title":"Unlearning Protected User Attributes in Recommendations with Adversarial Training","date":"2022-06-09","arxiv_id":"2206.04500","repositories_listed":1,"syntology":null},{"url":"/paper/cgmn-a-contrastive-graph-matching-network-for","slug":"cgmn-a-contrastive-graph-matching-network-for","title":"CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning","date":"2022-05-30","arxiv_id":"2205.15083","repositories_listed":1,"syntology":null},{"url":"/paper/gdsrec-graph-based-decentralized","slug":"gdsrec-graph-based-decentralized","title":"GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation","date":"2022-05-20","arxiv_id":"2205.09948","repositories_listed":1,"syntology":null},{"url":"/paper/tensor-based-collaborative-filtering-with","slug":"tensor-based-collaborative-filtering-with","title":"Tensor-based Collaborative Filtering With Smooth Ratings Scale","date":"2022-05-10","arxiv_id":"2205.05070","repositories_listed":1,"syntology":null},{"url":"/paper/are-quantum-computers-practical-yet-a-case","slug":"are-quantum-computers-practical-yet-a-case","title":"Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks","date":"2022-05-09","arxiv_id":"2205.04490","repositories_listed":1,"syntology":null},{"url":"/paper/hypergraph-contrastive-collaborative","slug":"hypergraph-contrastive-collaborative","title":"Hypergraph Contrastive Collaborative Filtering","date":"2022-04-26","arxiv_id":"2204.12200","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-accuracy-novelty-performance","slug":"investigating-accuracy-novelty-performance","title":"Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering","date":"2022-04-26","arxiv_id":"2204.12326","repositories_listed":1,"syntology":null},{"url":"/paper/less-is-more-reweighting-important-spectral","slug":"less-is-more-reweighting-important-spectral","title":"Less is More: Reweighting Important Spectral Graph Features for Recommendation","date":"2022-04-24","arxiv_id":"2204.11346","repositories_listed":1,"syntology":null},{"url":"/paper/broad-recommender-system-an-efficient","slug":"broad-recommender-system-an-efficient","title":"Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach","date":"2022-04-20","arxiv_id":"2204.11602","repositories_listed":1,"syntology":null},{"url":"/paper/hrcf-enhancing-collaborative-filtering-via","slug":"hrcf-enhancing-collaborative-filtering-via","title":"HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization","date":"2022-04-18","arxiv_id":"2204.08176","repositories_listed":1,"syntology":null},{"url":"/paper/gram-fast-fine-tuning-of-pre-trained-language","slug":"gram-fast-fine-tuning-of-pre-trained-language","title":"GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering","date":"2022-04-08","arxiv_id":"2204.04179","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":4,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/gram-fast-fine-tuning-of-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2204.04179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.04179"}},"official":{"repos":["yoonseok312/gram"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ia-gcn-interactive-graph-convolutional","slug":"ia-gcn-interactive-graph-convolutional","title":"IA-GCN: Interactive Graph Convolutional Network for Recommendation","date":"2022-04-08","arxiv_id":"2204.03827","repositories_listed":1,"syntology":null},{"url":"/paper/negative-sampling-for-recommendation","slug":"negative-sampling-for-recommendation","title":"Bayesian Negative Sampling for Recommendation","date":"2022-04-02","arxiv_id":"2204.06520","repositories_listed":1,"syntology":null},{"url":"/paper/arerec-attentive-local-interaction-model-for","slug":"arerec-attentive-local-interaction-model-for","title":"ARERec: Attentive Local Interaction Model for Sequential Recommendation","date":"2022-03-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/distributed-memory-sparse-kernels-for-machine","slug":"distributed-memory-sparse-kernels-for-machine","title":"Distributed-Memory Sparse Kernels for Machine Learning","date":"2022-03-15","arxiv_id":"2203.07673","repositories_listed":1,"syntology":null},{"url":"/paper/consensus-learning-from-heterogeneous","slug":"consensus-learning-from-heterogeneous","title":"Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering","date":"2022-02-26","arxiv_id":"2202.13140","repositories_listed":1,"syntology":null},{"url":"/paper/tee-based-decentralized-recommender-systems","slug":"tee-based-decentralized-recommender-systems","title":"TEE-based decentralized recommender systems: The raw data sharing redemption","date":"2022-02-23","arxiv_id":"2202.11655","repositories_listed":1,"syntology":null},{"url":"/paper/improving-graph-collaborative-filtering-with","slug":"improving-graph-collaborative-filtering-with","title":"Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning","date":"2022-02-13","arxiv_id":"2202.06200","repositories_listed":1,"syntology":null},{"url":"/paper/consistent-collaborative-filtering-via-tensor","slug":"consistent-collaborative-filtering-via-tensor","title":"Consistent Collaborative Filtering via Tensor Decomposition","date":"2022-01-28","arxiv_id":"2201.11936","repositories_listed":1,"syntology":null},{"url":"/paper/explainability-in-music-recommender-systems","slug":"explainability-in-music-recommender-systems","title":"Explainability in Music Recommender Systems","date":"2022-01-25","arxiv_id":"2201.10528","repositories_listed":1,"syntology":null},{"url":"/paper/gan-based-matrix-factorization-for","slug":"gan-based-matrix-factorization-for","title":"GAN-based Matrix Factorization for Recommender Systems","date":"2022-01-20","arxiv_id":"2201.08042","repositories_listed":1,"syntology":null},{"url":"/paper/emergent-instabilities-in-algorithmic","slug":"emergent-instabilities-in-algorithmic","title":"Emergent Instabilities in Algorithmic Feedback Loops","date":"2022-01-18","arxiv_id":"2201.07203","repositories_listed":1,"syntology":null},{"url":"/paper/on-sampling-collaborative-filtering-datasets","slug":"on-sampling-collaborative-filtering-datasets","title":"On Sampling Collaborative Filtering Datasets","date":"2022-01-13","arxiv_id":"2201.04768","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-sampling-collaborative-filtering-datasets#ran","syntology_url":"https://syntology.ai/paper/2201.04768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.04768"}},"official":{"repos":["noveens/sampling_cf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/collaborative-reflection-augmented","slug":"collaborative-reflection-augmented","title":"Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems","date":"2022-01-10","arxiv_id":"2201.03158","repositories_listed":1,"syntology":null},{"url":"/paper/multi-behavior-enhanced-recommendation-with","slug":"multi-behavior-enhanced-recommendation-with","title":"Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling","date":"2022-01-07","arxiv_id":"2201.02307","repositories_listed":1,"syntology":null},{"url":"/paper/deep-attentional-guided-image-filtering","slug":"deep-attentional-guided-image-filtering","title":"Deep Attentional Guided Image Filtering","date":"2021-12-13","arxiv_id":"2112.06401","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-recommendation-as-language-modeling","slug":"zero-shot-recommendation-as-language-modeling","title":"Zero-Shot Recommendation as Language Modeling","date":"2021-12-08","arxiv_id":"2112.04184","repositories_listed":1,"syntology":null},{"url":"/paper/given-users-recommendations-based-on-reviews","slug":"given-users-recommendations-based-on-reviews","title":"Given Users Recommendations Based on Reviews on Yelp","date":"2021-12-03","arxiv_id":"2112.01762","repositories_listed":1,"syntology":null},{"url":"/paper/latent-structures-mining-with-contrastive","slug":"latent-structures-mining-with-contrastive","title":"Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation","date":"2021-11-01","arxiv_id":"2111.00678","repositories_listed":1,"syntology":null},{"url":"/paper/implicit-feedbacks-are-not-always-favorable","slug":"implicit-feedbacks-are-not-always-favorable","title":"Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions","date":"2021-10-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deconfounded-causal-collaborative-filtering","slug":"deconfounded-causal-collaborative-filtering","title":"Deconfounded Causal Collaborative Filtering","date":"2021-10-14","arxiv_id":"2110.07122","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-aware-coupled-graph-neural-network","slug":"knowledge-aware-coupled-graph-neural-network","title":"Knowledge-aware Coupled Graph Neural Network for Social Recommendation","date":"2021-10-08","arxiv_id":"2110.03987","repositories_listed":1,"syntology":null},{"url":"/paper/social-recommendation-with-self-supervised","slug":"social-recommendation-with-self-supervised","title":"Social Recommendation with Self-Supervised Metagraph Informax Network","date":"2021-10-08","arxiv_id":"2110.03958","repositories_listed":1,"syntology":null},{"url":"/paper/boost-rs-boosted-embeddings-for-recommender","slug":"boost-rs-boosted-embeddings-for-recommender","title":"Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction","date":"2021-09-28","arxiv_id":"2109.14766","repositories_listed":1,"syntology":null}],"record_sha256":"9514ae63d8b3f18c18450eed79b10456430a976d215131230e750ffd64a2f0be","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}