{"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/20","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":20,"pages_in_order":61,"rows_per_page":100,"rows":[1901,2000],"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/19","next":"/task/recommendation-systems/papers/21","papers":[{"url":"/paper/gated-attentive-autoencoder-for-content-aware","slug":"gated-attentive-autoencoder-for-content-aware","title":"Gated Attentive-Autoencoder for Content-Aware Recommendation","date":"2018-12-07","arxiv_id":"1812.02869","repositories_listed":1,"syntology":null},{"url":"/paper/top-k-off-policy-correction-for-a-reinforce","slug":"top-k-off-policy-correction-for-a-reinforce","title":"Top-K Off-Policy Correction for a REINFORCE Recommender System","date":"2018-12-06","arxiv_id":"1812.02353","repositories_listed":1,"syntology":null},{"url":"/paper/fighting-fire-with-fire-using-antidote-data","slug":"fighting-fire-with-fire-using-antidote-data","title":"Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems","date":"2018-12-02","arxiv_id":"1812.01504","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fighting-fire-with-fire-using-antidote-data#ran","syntology_url":"https://syntology.ai/paper/1812.01504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01504"}},"official":null}},{"url":"/paper/movie-recommendation-system-using-sentiment","slug":"movie-recommendation-system-using-sentiment","title":"Movie Recommendation System using Sentiment Analysis from Microblogging Data","date":"2018-11-27","arxiv_id":"1811.10804","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-variational-autoencoders-for","slug":"sequential-variational-autoencoders-for","title":"Sequential Variational Autoencoders for Collaborative Filtering","date":"2018-11-25","arxiv_id":"1811.09975","repositories_listed":1,"syntology":null},{"url":"/paper/deep-item-based-collaborative-filtering-for","slug":"deep-item-based-collaborative-filtering-for","title":"Deep Item-based Collaborative Filtering for Top-N Recommendation","date":"2018-11-11","arxiv_id":"1811.04392","repositories_listed":1,"syntology":null},{"url":"/paper/fast-non-bayesian-poisson-factorization-for","slug":"fast-non-bayesian-poisson-factorization-for","title":"Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations","date":"2018-11-05","arxiv_id":"1811.01908","repositories_listed":1,"syntology":null},{"url":"/paper/faster-matrix-completion-using-randomized-svd","slug":"faster-matrix-completion-using-randomized-svd","title":"Faster Matrix Completion Using Randomized SVD","date":"2018-10-16","arxiv_id":"1810.06860","repositories_listed":1,"syntology":null},{"url":"/paper/graphbtm-graph-enhanced-autoencoded","slug":"graphbtm-graph-enhanced-autoencoded","title":"GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/modelling-salient-features-as-directions-in","slug":"modelling-salient-features-as-directions-in","title":"Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/point-of-interest-recommendation-exploiting","slug":"point-of-interest-recommendation-exploiting","title":"Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence","date":"2018-09-27","arxiv_id":"1809.10770","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-approach-for-venue-recommendation","slug":"a-novel-approach-for-venue-recommendation","title":"A novel approach for venue recommendation using cross-domain techniques","date":"2018-09-26","arxiv_id":"1809.09864","repositories_listed":1,"syntology":null},{"url":"/paper/coupled-graphs-and-tensor-factorization-for","slug":"coupled-graphs-and-tensor-factorization-for","title":"Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection","date":"2018-09-22","arxiv_id":"1809.08353","repositories_listed":1,"syntology":null},{"url":"/paper/ranking-distillation-learning-compact-ranking","slug":"ranking-distillation-learning-compact-ranking","title":"Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System","date":"2018-09-19","arxiv_id":"1809.07428","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-deterministic-framework-for-non","slug":"a-novel-deterministic-framework-for-non","title":"A Novel Deterministic Framework for Non-probabilistic Recommender Systems","date":"2018-09-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cold-start-recommendations-in-collective","slug":"cold-start-recommendations-in-collective","title":"Cold-start recommendations in Collective Matrix Factorization","date":"2018-09-02","arxiv_id":"1809.00366","repositories_listed":1,"syntology":null},{"url":"/paper/spectral-collaborative-filtering","slug":"spectral-collaborative-filtering","title":"Spectral Collaborative Filtering","date":"2018-08-30","arxiv_id":"1808.10523","repositories_listed":1,"syntology":null},{"url":"/paper/vizml-a-machine-learning-approach-to","slug":"vizml-a-machine-learning-approach-to","title":"VizML: A Machine Learning Approach to Visualization Recommendation","date":"2018-08-14","arxiv_id":"1808.04819","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-personalized-ranking-for","slug":"adversarial-personalized-ranking-for","title":"Adversarial Personalized Ranking for Recommendation","date":"2018-08-12","arxiv_id":"1808.03908","repositories_listed":1,"syntology":null},{"url":"/paper/outer-product-based-neural-collaborative","slug":"outer-product-based-neural-collaborative","title":"Outer Product-based Neural Collaborative Filtering","date":"2018-08-12","arxiv_id":"1808.03912","repositories_listed":1,"syntology":null},{"url":"/paper/recogym-a-reinforcement-learning-environment","slug":"recogym-a-reinforcement-learning-environment","title":"RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising","date":"2018-08-02","arxiv_id":"1808.00720","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/recogym-a-reinforcement-learning-environment#ran","syntology_url":"https://syntology.ai/paper/1808.00720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.00720"}},"official":{"repos":["criteo-research/reco-gym"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-knowledge-based-filtering-story-recommender","slug":"a-knowledge-based-filtering-story-recommender","title":"An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise","date":"2018-07-31","arxiv_id":"1808.00103","repositories_listed":1,"syntology":null},{"url":"/paper/kb4rec-a-dataset-for-linking-knowledge-bases","slug":"kb4rec-a-dataset-for-linking-knowledge-bases","title":"KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems","date":"2018-07-30","arxiv_id":"1807.11141","repositories_listed":1,"syntology":null},{"url":"/paper/the-rise-of-guardians-fact-checking-url-1","slug":"the-rise-of-guardians-fact-checking-url-1","title":"The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News","date":"2018-07-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-quantum-inspired-classical-algorithm-for","slug":"a-quantum-inspired-classical-algorithm-for","title":"A quantum-inspired classical algorithm for recommendation systems","date":"2018-07-10","arxiv_id":"1807.04271","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":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) · 0 unverified","sample_list":"/paper/a-quantum-inspired-classical-algorithm-for#ran","syntology_url":"https://syntology.ai/paper/1807.04271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.04271"}},"official":null}},{"url":"/paper/a-unified-framework-for-structured-low-rank","slug":"a-unified-framework-for-structured-low-rank","title":"A Unified Framework for Structured Low-rank Matrix Learning","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/accelerated-spectral-ranking","slug":"accelerated-spectral-ranking","title":"Accelerated Spectral Ranking","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/augment-and-reduce-stochastic-inference-for","slug":"augment-and-reduce-stochastic-inference-for","title":"Augment and Reduce: Stochastic Inference for Large Categorical Distributions","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/personalized-review-generation-by-expanding","slug":"personalized-review-generation-by-expanding","title":"Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-the-query-set-on-the-evaluation-of","slug":"impact-of-the-query-set-on-the-evaluation-of","title":"Impact of the Query Set on the Evaluation of Expert Finding Systems","date":"2018-06-28","arxiv_id":"1806.10813","repositories_listed":1,"syntology":null},{"url":"/paper/conversational-recommender-system","slug":"conversational-recommender-system","title":"Conversational Recommender System","date":"2018-06-08","arxiv_id":"1806.03277","repositories_listed":1,"syntology":null},{"url":"/paper/jtav-jointly-learning-social-media-content","slug":"jtav-jointly-learning-social-media-content","title":"JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features","date":"2018-06-05","arxiv_id":"1806.01483","repositories_listed":1,"syntology":null},{"url":"/paper/learning-contextual-bandits-in-a-non","slug":"learning-contextual-bandits-in-a-non","title":"Learning Contextual Bandits in a Non-stationary Environment","date":"2018-05-23","arxiv_id":"1805.09365","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-mean-embedding-a-kernel-method","slug":"counterfactual-mean-embedding-a-kernel-method","title":"Counterfactual Mean Embeddings","date":"2018-05-22","arxiv_id":"1805.08845","repositories_listed":1,"syntology":null},{"url":"/paper/npe-neural-personalized-embedding-for","slug":"npe-neural-personalized-embedding-for","title":"NPE: Neural Personalized Embedding for Collaborative Filtering","date":"2018-05-17","arxiv_id":"1805.06563","repositories_listed":1,"syntology":null},{"url":"/paper/conet-collaborative-cross-networks-for-cross","slug":"conet-collaborative-cross-networks-for-cross","title":"CoNet: Collaborative Cross Networks for Cross-Domain Recommendation","date":"2018-04-18","arxiv_id":"1804.06769","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-twitter-user-socioeconomic","slug":"predicting-twitter-user-socioeconomic","title":"Predicting Twitter User Socioeconomic Attributes with Network and Language Information","date":"2018-04-11","arxiv_id":"1804.04095","repositories_listed":1,"syntology":null},{"url":"/paper/learning-over-knowledge-base-embeddings-for","slug":"learning-over-knowledge-base-embeddings-for","title":"Learning over Knowledge-Base Embeddings for Recommendation","date":"2018-03-17","arxiv_id":"1803.06540","repositories_listed":1,"syntology":null},{"url":"/paper/deep-models-of-interactions-across-sets","slug":"deep-models-of-interactions-across-sets","title":"Deep Models of Interactions Across Sets","date":"2018-03-07","arxiv_id":"1803.02879","repositories_listed":1,"syntology":null},{"url":"/paper/relative-pairwise-relationship-constrained","slug":"relative-pairwise-relationship-constrained","title":"Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation","date":"2018-03-05","arxiv_id":"1803.02218","repositories_listed":1,"syntology":null},{"url":"/paper/sql-rank-a-listwise-approach-to-collaborative","slug":"sql-rank-a-listwise-approach-to-collaborative","title":"SQL-Rank: A Listwise Approach to Collaborative Ranking","date":"2018-02-28","arxiv_id":"1803.00114","repositories_listed":1,"syntology":null},{"url":"/paper/federated-meta-learning-for-recommendation","slug":"federated-meta-learning-for-recommendation","title":"Federated Meta-Learning with Fast Convergence and Efficient Communication","date":"2018-02-22","arxiv_id":"1802.07876","repositories_listed":1,"syntology":null},{"url":"/paper/augment-and-reduce-stochastic-inference-for-1","slug":"augment-and-reduce-stochastic-inference-for-1","title":"Augment and Reduce: Stochastic Inference for Large Categorical Distributions","date":"2018-02-12","arxiv_id":"1802.04220","repositories_listed":1,"syntology":null},{"url":"/paper/memory-augmented-neural-networks-for","slug":"memory-augmented-neural-networks-for","title":"DeepProcess: Supporting business process execution using a MANN-based recommender system","date":"2018-02-03","arxiv_id":"1802.00938","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-heterogeneous-side-information","slug":"learning-with-heterogeneous-side-information","title":"Side Information Fusion for Recommender Systems over Heterogeneous Information Network","date":"2018-01-08","arxiv_id":"1801.02411","repositories_listed":1,"syntology":null},{"url":"/paper/use-of-deep-learning-in-modern-recommendation","slug":"use-of-deep-learning-in-modern-recommendation","title":"Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works","date":"2017-12-20","arxiv_id":"1712.07525","repositories_listed":1,"syntology":null},{"url":"/paper/context-selection-for-embedding-models","slug":"context-selection-for-embedding-models","title":"Context Selection for Embedding Models","date":"2017-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pixie-a-system-for-recommending-3-billion","slug":"pixie-a-system-for-recommending-3-billion","title":"Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time","date":"2017-11-21","arxiv_id":"1711.07601","repositories_listed":1,"syntology":null},{"url":"/paper/rdf2vec-rdf-graph-embeddings-and-their","slug":"rdf2vec-rdf-graph-embeddings-and-their","title":"RDF2Vec: RDF Graph Embeddings and Their Applications","date":"2017-11-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weighted-svd-matrix-factorization-with","slug":"weighted-svd-matrix-factorization-with","title":"Weighted-SVD: Matrix Factorization with Weights on the Latent Factors","date":"2017-10-02","arxiv_id":"1710.00482","repositories_listed":1,"syntology":null},{"url":"/paper/recommender-system-for-weld-break-prediction","slug":"recommender-system-for-weld-break-prediction","title":"Recommender system for Weld break prediction","date":"2017-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sweetrs-dataset-for-a-recommender-systems-of","slug":"sweetrs-dataset-for-a-recommender-systems-of","title":"SweetRS: Dataset for a recommender systems of sweets","date":"2017-09-10","arxiv_id":"1709.03496","repositories_listed":1,"syntology":null},{"url":"/paper/an-influence-receptivity-model-for-topic","slug":"an-influence-receptivity-model-for-topic","title":"Estimation of a Low-rank Topic-Based Model for Information Cascades","date":"2017-09-06","arxiv_id":"1709.01919","repositories_listed":1,"syntology":null},{"url":"/paper/style2vec-representation-learning-for-fashion","slug":"style2vec-representation-learning-for-fashion","title":"Style2Vec: Representation Learning for Fashion Items from Style Sets","date":"2017-08-14","arxiv_id":"1708.04014","repositories_listed":1,"syntology":null},{"url":"/paper/latent-relational-metric-learning-via-memory","slug":"latent-relational-metric-learning-via-memory","title":"Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking","date":"2017-07-17","arxiv_id":"1707.05176","repositories_listed":1,"syntology":null},{"url":"/paper/translation-based-recommendation","slug":"translation-based-recommendation","title":"Translation-based Recommendation","date":"2017-07-08","arxiv_id":"1707.02410","repositories_listed":1,"syntology":null},{"url":"/paper/inter-session-modeling-for-session-based","slug":"inter-session-modeling-for-session-based","title":"Inter-Session Modeling for Session-Based Recommendation","date":"2017-06-22","arxiv_id":"1706.07506","repositories_listed":1,"syntology":null},{"url":"/paper/pyreclab-a-software-library-for-quick","slug":"pyreclab-a-software-library-for-quick","title":"pyRecLab: A Software Library for Quick Prototyping of Recommender Systems","date":"2017-06-20","arxiv_id":"1706.06291","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learning-for-resampling-recommendation","slug":"meta-learning-for-resampling-recommendation","title":"Meta-Learning for Resampling Recommendation Systems","date":"2017-06-06","arxiv_id":"1706.02289","repositories_listed":1,"syntology":null},{"url":"/paper/to-index-or-not-to-index-optimizing-exact","slug":"to-index-or-not-to-index-optimizing-exact","title":"To Index or Not to Index: Optimizing Exact Maximum Inner Product Search","date":"2017-06-05","arxiv_id":"1706.01449","repositories_listed":1,"syntology":null},{"url":"/paper/the-sample-complexity-of-online-one-class","slug":"the-sample-complexity-of-online-one-class","title":"The Sample Complexity of Online One-Class Collaborative Filtering","date":"2017-05-31","arxiv_id":"1706.00061","repositories_listed":1,"syntology":null},{"url":"/paper/zonotope-hit-and-run-for-efficient-sampling","slug":"zonotope-hit-and-run-for-efficient-sampling","title":"Zonotope hit-and-run for efficient sampling from projection DPPs","date":"2017-05-30","arxiv_id":"1705.10498","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-parity-fairness-objectives-for","slug":"beyond-parity-fairness-objectives-for","title":"Beyond Parity: Fairness Objectives for Collaborative Filtering","date":"2017-05-24","arxiv_id":"1705.08804","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-and-pairwise-ranking","slug":"representation-learning-and-pairwise-ranking","title":"Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems","date":"2017-04-29","arxiv_id":"1705.00105","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-poisson-factorization-for-temporal","slug":"recurrent-poisson-factorization-for-temporal","title":"Recurrent Poisson Factorization for Temporal Recommendation","date":"2017-03-04","arxiv_id":"1703.01442","repositories_listed":1,"syntology":null},{"url":"/paper/human-interaction-with-recommendation-systems","slug":"human-interaction-with-recommendation-systems","title":"Human Interaction with Recommendation Systems","date":"2017-03-01","arxiv_id":"1703.00535","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/human-interaction-with-recommendation-systems#ran","syntology_url":"https://syntology.ai/paper/1703.00535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.00535"}},"official":{"repos":["schmit/human_interaction"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/predicting-pairwise-relations-with-neural","slug":"predicting-pairwise-relations-with-neural","title":"Predicting Pairwise Relations with Neural Similarity Encoders","date":"2017-02-06","arxiv_id":"1702.01824","repositories_listed":1,"syntology":null},{"url":"/paper/equivalence-of-restricted-boltzmann-machines","slug":"equivalence-of-restricted-boltzmann-machines","title":"Equivalence of restricted Boltzmann machines and tensor network states","date":"2017-01-17","arxiv_id":"1701.04831","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-video-recommendation-using-rich","slug":"personalized-video-recommendation-using-rich","title":"Personalized Video Recommendation Using Rich Contents from Videos","date":"2016-12-21","arxiv_id":"1612.06935","repositories_listed":1,"syntology":null},{"url":"/paper/split-door-criterion-identification-of-causal","slug":"split-door-criterion-identification-of-causal","title":"Split-door criterion: Identification of causal effects through auxiliary outcomes","date":"2016-11-28","arxiv_id":"1611.09414","repositories_listed":1,"syntology":null},{"url":"/paper/top-n-recommendation-on-graphs","slug":"top-n-recommendation-on-graphs","title":"Top-N Recommendation on Graphs","date":"2016-09-27","arxiv_id":"1609.08264","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-matrix-factorization-for","slug":"convolutional-matrix-factorization-for","title":"Convolutional Matrix Factorization for Document Context-Aware Recommendation","date":"2016-09-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/temporal-learning-and-sequence-modeling-for-a","slug":"temporal-learning-and-sequence-modeling-for-a","title":"Temporal Learning and Sequence Modeling for a Job Recommender System","date":"2016-08-11","arxiv_id":"1608.03333","repositories_listed":1,"syntology":null},{"url":"/paper/point-of-interest-recommendations-learning","slug":"point-of-interest-recommendations-learning","title":"Point-of-Interest Recommendations: Learning Potential Check-ins from Friends","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/incremental-factorization-machines-for","slug":"incremental-factorization-machines-for","title":"Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation","date":"2016-07-11","arxiv_id":"1607.02858","repositories_listed":1,"syntology":null},{"url":"/paper/dictionary-learning-for-massive-matrix","slug":"dictionary-learning-for-massive-matrix","title":"Dictionary Learning for Massive Matrix Factorization","date":"2016-05-03","arxiv_id":"1605.00937","repositories_listed":1,"syntology":null},{"url":"/paper/towards-bayesian-deep-learning-a-survey","slug":"towards-bayesian-deep-learning-a-survey","title":"A Survey on Bayesian Deep Learning","date":"2016-04-06","arxiv_id":"1604.01662","repositories_listed":1,"syntology":null},{"url":"/paper/the-influence-of-frequency-recency-and","slug":"the-influence-of-frequency-recency-and","title":"The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems","date":"2016-04-04","arxiv_id":"1604.00837","repositories_listed":1,"syntology":null},{"url":"/paper/learning-compatibility-across-categories-for","slug":"learning-compatibility-across-categories-for","title":"Learning Compatibility Across Categories for Heterogeneous Item Recommendation","date":"2016-03-31","arxiv_id":"1603.09473","repositories_listed":1,"syntology":null},{"url":"/paper/a-multinomial-probabilistic-model-for-movie","slug":"a-multinomial-probabilistic-model-for-movie","title":"A multinomial probabilistic model for movie genre predictions","date":"2016-03-25","arxiv_id":"1603.07849","repositories_listed":1,"syntology":null},{"url":"/paper/cascading-bandits-for-large-scale","slug":"cascading-bandits-for-large-scale","title":"Cascading Bandits for Large-Scale Recommendation Problems","date":"2016-03-17","arxiv_id":"1603.05359","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-voting-promotes-collective","slug":"sequential-voting-promotes-collective","title":"Sequential Voting Promotes Collective Discovery in Social Recommendation Systems","date":"2016-03-14","arxiv_id":"1603.04466","repositories_listed":1,"syntology":null},{"url":"/paper/top-n-recommendation-with-novel-rank","slug":"top-n-recommendation-with-novel-rank","title":"Top-N Recommendation with Novel Rank Approximation","date":"2016-02-25","arxiv_id":"1602.07783","repositories_listed":1,"syntology":null},{"url":"/paper/algorithmic-acceleration-of-parallel-als-for","slug":"algorithmic-acceleration-of-parallel-als-for","title":"Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark","date":"2016-01-10","arxiv_id":"1508.03110","repositories_listed":1,"syntology":null},{"url":"/paper/song-recommendation-with-non-negative-matrix","slug":"song-recommendation-with-non-negative-matrix","title":"Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation","date":"2016-01-08","arxiv_id":"1601.01892","repositories_listed":1,"syntology":null},{"url":"/paper/fast-k-nn-search","slug":"fast-k-nn-search","title":"Fast k-NN search","date":"2015-09-23","arxiv_id":"1509.06957","repositories_listed":1,"syntology":null},{"url":"/paper/rank-geofm-a-ranking-based-geographical","slug":"rank-geofm-a-ranking-based-geographical","title":"Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation","date":"2015-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sgrank-combining-statistical-and-graphical","slug":"sgrank-combining-statistical-and-graphical","title":"SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction","date":"2015-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-view-deep-learning-approach-for-cross","slug":"a-multi-view-deep-learning-approach-for-cross","title":"A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems","date":"2015-05-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fastfm-a-library-for-factorization-machines","slug":"fastfm-a-library-for-factorization-machines","title":"fastFM: A Library for Factorization Machines","date":"2015-05-04","arxiv_id":"1505.00641","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-deep-learning-for-recommender","slug":"collaborative-deep-learning-for-recommender","title":"Collaborative Deep Learning for Recommender Systems","date":"2014-09-10","arxiv_id":"1409.2944","repositories_listed":1,"syntology":null},{"url":"/paper/location-recommendation-in-location-based","slug":"location-recommendation-in-location-based","title":"Location Recommendation in Location-based Social Networks using User Check-in Data","date":"2013-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-random-walk-approach-to-selectional","slug":"a-random-walk-approach-to-selectional","title":"A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation","date":"2013-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/recommender-systems","slug":"recommender-systems","title":"Recommender Systems","date":"2012-02-06","arxiv_id":"1202.1112","repositories_listed":1,"syntology":null},{"url":"/paper/solving-the-apparent-diversity-accuracy","slug":"solving-the-apparent-diversity-accuracy","title":"Solving the apparent diversity-accuracy dilemma of recommender systems","date":"2010-03-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/factorization-machines-1","slug":"factorization-machines-1","title":"Factorization Machines","date":"2010-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/restricted-boltzmann-machines-for","slug":"restricted-boltzmann-machines-for","title":"Restricted Boltzmann Machines for Collaborative Filtering","date":"2007-06-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"ip2-entity-guided-interest-probing-for","title":"IP2: Entity-Guided Interest Probing for Personalized News Recommendation","date":"2025-07-18","arxiv_id":"2507.13622","repositories_listed":0,"syntology":null},{"url":null,"slug":"sgcl-unifying-self-supervised-and-supervised","title":"SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation","date":"2025-07-17","arxiv_id":"2507.13336","repositories_listed":0,"syntology":null},{"url":null,"slug":"looking-for-fairness-in-recommender-systems","title":"Looking for Fairness in Recommender Systems","date":"2025-07-16","arxiv_id":"2507.12242","repositories_listed":0,"syntology":null}],"record_sha256":"6f822cb882afb3dfb2990d26e8de49a8b27029afda28211dc74d3faacd131adb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}