{"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/59","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":59,"pages_in_order":61,"rows_per_page":100,"rows":[5801,5900],"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/58","next":"/task/recommendation-systems/papers/60","papers":[{"url":null,"slug":"towards-bayesian-deep-learning-a-framework","title":"Towards Bayesian Deep Learning: A Framework and Some Existing Methods","date":"2016-08-24","arxiv_id":"1608.06884","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-deep-space-learning-personalized","title":"Exploring Deep Space: Learning Personalized Ranking in a Semantic Space","date":"2016-08-22","arxiv_id":"1608.00276","repositories_listed":0,"syntology":null},{"url":null,"slug":"infusing-collaborative-recommenders-with","title":"Infusing Collaborative Recommenders with Distributed Representations","date":"2016-08-22","arxiv_id":"1608.06298","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-trust-aware-neighbourhood-in-trust","title":"Exploring Trust-Aware Neighbourhood in Trust-based Recommendation","date":"2016-08-18","arxiv_id":"1608.05380","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-integrated-recommender-algorithm-for","title":"An Integrated Recommender Algorithm for Rating Prediction","date":"2016-08-05","arxiv_id":"1608.02021","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-annotation-complexity-the-case-of","title":"Addressing Annotation Complexity: The Case of Annotating Ideological Perspective in Egyptian Social Media","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-biases-in-human-perception-of-user","title":"Analyzing Biases in Human Perception of User Age and Gender from Text","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"claim-synthesis-via-predicate-recycling","title":"Claim Synthesis via Predicate Recycling","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inferring-perceived-demographics-from-user","title":"Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pigeo-a-python-geotagging-tool","title":"pigeo: A Python Geotagging Tool","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"which-tumblr-post-should-i-read-next","title":"Which Tumblr Post Should I Read Next?","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-span-for-personalisation","title":"Attention Span For Personalisation","date":"2016-07-30","arxiv_id":"1608.00147","repositories_listed":0,"syntology":null},{"url":null,"slug":"polynomial-networks-and-factorization","title":"Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms","date":"2016-07-29","arxiv_id":"1607.08810","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-nonnegative-matrix-factorization-and","title":"Adaptive Nonnegative Matrix Factorization and Measure Comparisons for Recommender Systems","date":"2016-07-26","arxiv_id":"1607.07607","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-movie-recommendations-solving-the","title":"Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations","date":"2016-07-26","arxiv_id":"1607.07904","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-personality-aware-recommendation","title":"Towards Personality-Aware Recommendation","date":"2016-07-23","arxiv_id":"1607.05088","repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-recommender-systems","title":"Streaming Recommender Systems","date":"2016-07-21","arxiv_id":"1607.06182","repositories_listed":0,"syntology":null},{"url":null,"slug":"vista-a-visually-socially-and-temporally","title":"Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation","date":"2016-07-15","arxiv_id":"1607.04373","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-recommender-system-based-on-personal","title":"Hybrid Recommender System Based on Personal Behavior Mining","date":"2016-07-10","arxiv_id":"1607.02754","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-session-recommendation-systems-with","title":"One-Shot Session Recommendation Systems with Combinatorial Items","date":"2016-07-05","arxiv_id":"1607.01381","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-statistical-relational","title":"Application of Statistical Relational Learning to Hybrid Recommendation Systems","date":"2016-07-04","arxiv_id":"1607.01050","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-point-of-interest-recommendation","title":"A Survey of Point-of-interest Recommendation in Location-based Social Networks","date":"2016-07-03","arxiv_id":"1607.00647","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-based-rating-prediction","title":"Review Based Rating Prediction","date":"2016-06-30","arxiv_id":"1607.00024","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-based-top-n-recommendation-using","title":"Content-Based Top-N Recommendation using Heterogeneous Relations","date":"2016-06-27","arxiv_id":"1606.08104","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-based-next-song-recommendation","title":"Neural Network Based Next-Song Recommendation","date":"2016-06-24","arxiv_id":"1606.07722","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-restricted-boltzmann-machines-for","title":"Explainable Restricted Boltzmann Machines for Collaborative Filtering","date":"2016-06-22","arxiv_id":"1606.07129","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-an-interpretable-recommender-via","title":"Building an Interpretable Recommender via Loss-Preserving Transformation","date":"2016-06-19","arxiv_id":"1606.05819","repositories_listed":0,"syntology":null},{"url":null,"slug":"gt-seer-geo-temporal-sequential-embedding","title":"GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation","date":"2016-06-19","arxiv_id":"1606.05859","repositories_listed":0,"syntology":null},{"url":null,"slug":"llfr-a-lanczos-based-latent-factor","title":"LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios","date":"2016-06-14","arxiv_id":"1606.04335","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-low-rank-approximation-approach-to-learning","title":"A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bit-at-semeval-2016-task-1-sentence","title":"BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deconstructing-complex-search-tasks-a","title":"Deconstructing Complex Search Tasks: a Bayesian Nonparametric Approach for Extracting Sub-tasks","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"making-sense-of-massive-amounts-of-scientific","title":"Making Sense of Massive Amounts of Scientific Publications: the Scientific Knowledge Miner Project","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mib-at-semeval-2016-task-4a-exploiting","title":"mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-papers-should-i-cite-from-my-reading","title":"What Papers Should I Cite from my Reading List? User Evaluation of a Manuscript Preparatory Assistive Task","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-bayesian-collaborative-topic","title":"Online Bayesian Collaborative Topic Regression","date":"2016-05-28","arxiv_id":"1605.08872","repositories_listed":0,"syntology":null},{"url":null,"slug":"matrix-completion-has-no-spurious-local","title":"Matrix Completion has No Spurious Local Minimum","date":"2016-05-24","arxiv_id":"1605.07272","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-content-based-recommendation-and-user","title":"On Content-Based Recommendation and User Privacy in Social-Tagging Systems","date":"2016-05-20","arxiv_id":"1605.06538","repositories_listed":0,"syntology":null},{"url":null,"slug":"competitive-analysis-of-the-top-k-ranking","title":"Competitive analysis of the top-K ranking problem","date":"2016-05-12","arxiv_id":"1605.03933","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-clustering-bandits-for-recommendation","title":"Graph Clustering Bandits for Recommendation","date":"2016-05-02","arxiv_id":"1605.00596","repositories_listed":0,"syntology":null},{"url":null,"slug":"rankdcg-rank-ordering-evaluation-measure","title":"RankDCG: Rank-Ordering Evaluation Measure","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"matrix-factorization-method-for-decentralized","title":"Matrix Factorization Method for Decentralized Recommender Systems","date":"2016-04-28","arxiv_id":"1604.08420","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-differentially-private-rating-collection","title":"Two Differentially Private Rating Collection Mechanisms for Recommender Systems","date":"2016-04-28","arxiv_id":"1604.08402","repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-based-approach-to-introduce","title":"Feedback-based Approach to Introduce Freshness in Recommendations","date":"2016-04-26","arxiv_id":"1604.07521","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-user-preference-for-social-image","title":"Analyzing User Preference for Social Image Recommendation","date":"2016-04-24","arxiv_id":"1604.07044","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-matrix-factorization-with-social","title":"Dynamic matrix factorization with social influence","date":"2016-04-21","arxiv_id":"1604.06194","repositories_listed":0,"syntology":null},{"url":null,"slug":"sherlock-sparse-hierarchical-embeddings-for","title":"Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering","date":"2016-04-20","arxiv_id":"1604.05813","repositories_listed":0,"syntology":null},{"url":null,"slug":"profiling-vs-time-vs-content-what-does-matter","title":"Profiling vs. Time vs. Content: What does Matter for Top-k Publication Recommendation based on Twitter Profiles? - An Extended Technical Report","date":"2016-04-18","arxiv_id":"1603.07016","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-compound-poisson-factorization","title":"Hierarchical Compound Poisson Factorization","date":"2016-04-13","arxiv_id":"1604.03853","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unbiased-data-collection-and-content","title":"An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization","date":"2016-04-12","arxiv_id":"1604.03506","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-matrix-recovery-via-the","title":"Structured Matrix Recovery via the Generalized Dantzig Selector","date":"2016-04-12","arxiv_id":"1604.03492","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-relational-learning-at-scale-with-admm","title":"Multi-Relational Learning at Scale with ADMM","date":"2016-04-03","arxiv_id":"1604.00647","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-recommendation-to-users-that-react","title":"Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs","date":"2016-03-30","arxiv_id":"1603.09233","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditor1-topic-maps-and-dita-labelling-tool","title":"CONDITOR1: Topic Maps and DITA labelling tool for textual documents with historical information","date":"2016-03-23","arxiv_id":"1603.07313","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-methods-and-recommender-systems","title":"Tensor Methods and Recommender Systems","date":"2016-03-19","arxiv_id":"1603.06038","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-optimal-social-dependency-for","title":"Learning Optimal Social Dependency for Recommendation","date":"2016-03-15","arxiv_id":"1603.04522","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-towards-debiasing-user-ratings","title":"An approach towards debiasing user ratings","date":"2016-03-14","arxiv_id":"1603.04482","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neutrosophic-recommender-system-for-medical","title":"A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures","date":"2016-02-25","arxiv_id":"1602.08447","repositories_listed":0,"syntology":null},{"url":null,"slug":"requirements-engineering-for-general","title":"Requirements Engineering for General Recommender Systems","date":"2016-02-24","arxiv_id":"1511.05262","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-skill-embedding-for-personalized","title":"Latent Skill Embedding for Personalized Lesson Sequence Recommendation","date":"2016-02-23","arxiv_id":"1602.07029","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovering-structured-probability-matrices","title":"Recovering Structured Probability Matrices","date":"2016-02-21","arxiv_id":"1602.06586","repositories_listed":0,"syntology":null},{"url":null,"slug":"11-x-11-domineering-is-solved-the-first","title":"11 x 11 Domineering is Solved: The first player wins","date":"2016-02-17","arxiv_id":"1602.05404","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommendations-as-treatments-debiasing","title":"Recommendations as Treatments: Debiasing Learning and Evaluation","date":"2016-02-17","arxiv_id":"1602.05352","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-harmonic-extension-approach-for","title":"A Harmonic Extension Approach for Collaborative Ranking","date":"2016-02-16","arxiv_id":"1602.05127","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-online-decision-making-with","title":"Data-Driven Online Decision Making with Costly Information Acquisition","date":"2016-02-11","arxiv_id":"1602.03600","repositories_listed":0,"syntology":null},{"url":null,"slug":"package-equivalence-in-complex-software","title":"Package equivalence in complex software network","date":"2016-02-11","arxiv_id":"1602.03681","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-filtering-via-sparse-markov","title":"Collaborative filtering via sparse Markov random fields","date":"2016-02-09","arxiv_id":"1602.02842","repositories_listed":0,"syntology":null},{"url":"/paper/ups-and-downs-modeling-the-visual-evolution","slug":"ups-and-downs-modeling-the-visual-evolution","title":"Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering","date":"2016-02-04","arxiv_id":"1602.01585","repositories_listed":0,"syntology":null},{"url":null,"slug":"socially-driven-news-recommendation","title":"Socially Driven News Recommendation","date":"2016-01-29","arxiv_id":"1506.01743","repositories_listed":0,"syntology":null},{"url":null,"slug":"recommender-systems-inspired-by-the-structure","title":"Recommender systems inspired by the structure of quantum theory","date":"2016-01-22","arxiv_id":"1601.06035","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-and-characterizing-mobility","title":"Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data","date":"2016-01-20","arxiv_id":"1601.05274","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-n-recommender-system-via-matrix","title":"Top-N Recommender System via Matrix Completion","date":"2016-01-19","arxiv_id":"1601.04800","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-synthetic-approach-for-recommendation","title":"A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews","date":"2016-01-11","arxiv_id":"1601.02327","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-a-robust-diversity-based-model-to","title":"Toward a Robust Diversity-Based Model to Detect Changes of Context","date":"2016-01-08","arxiv_id":"1601.01917","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-recommendation-algorithm-for-social","title":"A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case","date":"2015-12-28","arxiv_id":"1512.08325","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-the-top-quality-item-through-crowd","title":"Selecting the top-quality item through crowd scoring","date":"2015-12-23","arxiv_id":"1512.07487","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-with-attention","title":"Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions","date":"2015-12-03","arxiv_id":"1512.01124","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-collaborative-filtering-approach-to-real","title":"A Collaborative Filtering Approach to Real-Time Hand Pose Estimation","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-thompson-sampling-for-online-matrix","title":"Efficient Thompson Sampling for Online Matrix-Factorization Recommendation","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/learning-image-and-user-features-for","slug":"learning-image-and-user-features-for","title":"Learning Image and User Features for Recommendation in Social Networks","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-adspace-posts-category-classification","title":"Online Adspace Posts' Category Classification","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ruchi-rating-individual-food-items-in","title":"Ruchi: Rating Individual Food Items in Restaurant Reviews","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"time-sensitive-recommendation-from-recurrent","title":"Time-Sensitive Recommendation From Recurrent User Activities","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-music-playlists","title":"Understanding Music Playlists","date":"2015-11-22","arxiv_id":"1511.07004","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-n-recommendations-from-expressive","title":"Top-N recommendations from expressive recommender systems","date":"2015-11-20","arxiv_id":"1511.06718","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-based-recommendation-algorithms-a","title":"Network-based recommendation algorithms: A review","date":"2015-11-19","arxiv_id":"1511.06252","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-collaborative-ranking-with","title":"Semi-supervised Collaborative Ranking with Push at Top","date":"2015-11-17","arxiv_id":"1511.05266","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-use-of-machine-learning-algorithms-in","title":"The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review","date":"2015-11-17","arxiv_id":"1511.05263","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-iterative-reweighted-method-for-tucker","title":"An Iterative Reweighted Method for Tucker Decomposition of Incomplete Multiway Tensors","date":"2015-11-15","arxiv_id":"1511.04695","repositories_listed":0,"syntology":null},{"url":null,"slug":"expressive-recommender-systems-through","title":"Expressive recommender systems through normalized nonnegative models","date":"2015-11-15","arxiv_id":"1511.04775","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-privileged-information-to-improve","title":"Combining Privileged Information to Improve Context-Aware Recommender Systems","date":"2015-11-07","arxiv_id":"1511.02290","repositories_listed":0,"syntology":null},{"url":null,"slug":"factorizing-lambdamart-for-cold-start","title":"Factorizing LambdaMART for cold start recommendations","date":"2015-11-04","arxiv_id":"1511.01282","repositories_listed":0,"syntology":null},{"url":null,"slug":"study-of-a-bias-in-the-offline-evaluation-of","title":"Study of a bias in the offline evaluation of a recommendation algorithm","date":"2015-11-04","arxiv_id":"1511.01280","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-collaborative-filtering-from-implicit","title":"Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees","date":"2015-11-03","arxiv_id":"1511.00792","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-shortlists-to-support-decision-making","title":"Using Shortlists to Support Decision Making and Improve Recommender System Performance","date":"2015-10-26","arxiv_id":"1510.07545","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-complexity-of-inner-product-similarity","title":"On the Complexity of Inner Product Similarity Join","date":"2015-10-09","arxiv_id":"1510.02824","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-consumer-behavior-data-to-reduce-energy","title":"Using consumer behavior data to reduce energy consumption in smart homes","date":"2015-10-01","arxiv_id":"1510.00165","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-saving-in-smart-homes-based-on","title":"Energy saving in smart homes based on consumer behaviour: A case study","date":"2015-09-18","arxiv_id":"1509.05722","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-curated-image-collections-modeling-and","title":"User-Curated Image Collections: Modeling and Recommendation","date":"2015-09-18","arxiv_id":"1509.05671","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-poisson-factorization","title":"Dynamic Poisson Factorization","date":"2015-09-15","arxiv_id":"1509.04640","repositories_listed":0,"syntology":null}],"record_sha256":"fab2d8b9c8cd301c1ece04a36ea49d8c7e18f9ed215917736aab7b14d44f22d9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}