recsys 2019 论文列表
Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019.
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Recommender system for developing new preferences and goals.
Recommender systems for contextually-aware, versioned items.
Exploiting contextual information for recommender systems oriented to tourism.
Revisiting offline evaluation for implicit-feedback recommender systems.
User's activity driven short-term context inference.
Music cold-start and long-tail recommendation: bias in deep representations.
Concept to code: deep learning for multitask recommendation.
SMORe: modularize graph embedding for recommendation.
Recommendations in a marketplace.
Multi-stakeholder recommendations: case studies, methods and challenges.
Fairness and discrimination in recommendation and retrieval.
Bandit algorithms in recommender systems.
ACM RecSys'19 late-breaking results (posters).
RecSys challenge 2019: session-based hotel recommendations.
REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation.
Recommendation in multistakeholder environments.
RecTour 2019: workshop on recommenders in tourism.
ORSUM 2019 2nd workshop on online recommender systems and user modeling.
RecSys '19 joint workshop on interfaces and human decision making for recommender systems.
The 7th international workshop on news recommendation and analytics (INRA 2019).
First workshop on the impact of recommender systems at ACM RecSys 2019.
Fourth international workshop on health recommender systems (HealthRecSys 2019).
Workshop on recommender systems in fashion (fashionXrecsys2019).
Third workshop on recommendation in complex scenarios (ComplexRec 2019).
Workshop on context-aware recommender systems.
Towards interactive recommending in model-based collaborative filtering systems.
StoryTime: eliciting preferences from children for book recommendations.
Microsoft recommenders: tools to accelerate developing recommender systems.
IRF: interactive recommendation through dialogue.
Interactive evaluation of recommender systems with SNIPER: an episode mining approach.
FineNet: a joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items.
Darwin & Goliath: a white-label recommender-system as-a-service with automated algorithm-selection.
AnnoMath TeX - a formula identifier annotation recommender system for STEM documents.
Driving content recommendations by building a knowledge base using weak supervision and transfer learning.
Incorporating intent propensities in personalized next best action recommendation.
Recommendation systems compliant with legal and editorial policies: the BBC+ app journey.
Recommendation in home improvement industry, challenges and opportunities.
Homepage personalization at spotify.
Groupon finally explains why we showed those offers.
Designer-driven add-to-cart recommendations.
Future of in-vehicle recommendation systems @ Bosch.
"Just play something awesome": the personalization powering voice interactions at Pandora.
The trinity of luxury fashion recommendations: data, experts and experimentation.
Using AI to build communities around interests on LinkedIn.
User-centric evaluation of session-based recommendations for an automated radio station.
Traversing semantically annotated queries for task-oriented query recommendation.
Time slice imputation for personalized goal-based recommendation in higher education.
The influence of personal values on music taste: towards value-based music recommendations.
Should we embed?: a study on the online performance of utilizing embeddings for real-time job recommendations.
PyRecGym: a reinforcement learning gym for recommender systems.
Product collection recommendation in online retail.
Predicting user routines with masked dilated convolutions.
Predicting online performance of job recommender systems with offline evaluation.
Pick & merge: an efficient item filtering scheme for Windows store recommendations.
Personalized fairness-aware re-ranking for microlending.
Performance comparison of neural and non-neural approaches to session-based recommendation.
PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy.
PAL: a position-bias aware learning framework for CTR prediction in live recommender systems.
On the discriminative power of hyper-parameters in cross-validation and how to choose them.
On gossip-based information dissemination in pervasive recommender systems.
Music recommendations in hyperbolic space: an application of empirical bayes and hierarchical poincaré embeddings.
Multi-armed recommender system bandit ensembles.
Latent multi-criteria ratings for recommendations.
How can they know that?: a study of factors affecting the creepiness of recommendations.
Guiding creative design in online advertising.
Greedy optimized multileaving for personalization.
Find my next job: labor market recommendations using administrative big data.
Enhancing VAEs for collaborative filtering: flexible priors & gating mechanisms.
DualDiv: diversifying items and explanation styles in explainable hybrid recommendation.
Data mining for item recommendation in MOBA games.
Compositional network embedding for link prediction.
Combining text summarization and aspect-based sentiment analysis of users' reviews to justify recommendations.
Attribute-based evaluation for recommender systems: incorporating user and item attributes in evaluation metrics.
Asymmetric Bayesian personalized ranking for one-class collaborative filtering.
Aligning daily activities with personality: towards a recommender system for improving wellbeing.
Adversarial tensor factorization for context-aware recommendation.
A simple multi-armed nearest-neighbor bandit for interactive recommendation.
A generative model for review-based recommendations.
Quick and accurate attack detection in recommender systems through user attributes.
Variational low rank multinomials for collaborative filtering with side-information.
HybridSVD: when collaborative information is not enough.
Adversarial attacks on an oblivious recommender.
Attribute-aware non-linear co-embeddings of graph features.
Deep social collaborative filtering.
Uplift-based evaluation and optimization of recommenders.
When actions speak louder than clicks: a combined model of purchase probability and long-term customer satisfaction.
Leveraging post-click feedback for content recommendations.
Sampling-bias-corrected neural modeling for large corpus item recommendations.
Personalized diffusions for top-n recommendation.
Efficient similarity computation for collaborative filtering in dynamic environments.
Pace my race: recommendations for marathon running.
Online learning to rank for sequential music recommendation.
CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations.
Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems.
A recommender system for heterogeneous and time sensitive environment.
Collective embedding for neural context-aware recommender systems.
Ghosting: contextualized inline query completion in large scale retail search.
Addressing delayed feedback for continuous training with neural networks in CTR prediction.
Domain adaptation in display advertising: an application for partner cold-start.
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction.
LORE: a large-scale offer recommendation engine with eligibility and capacity constraints.
A comparison of calibrated and intent-aware recommendations.
Predictability limits in session-based next item recommendation.
Deep language-based critiquing for recommender systems.
Style conditioned recommendations.
Relaxed softmax for PU learning.
A deep learning system for predicting size and fit in fashion e-commerce.
Are we really making much progress? A worrying analysis of recent neural recommendation approaches.
User-centered evaluation of strategies for recommending sequences of points of interest to groups.
PrivateJobMatch: a privacy-oriented deferred multi-match recommender system for stable employment.
Efficient privacy-preserving recommendations based on social graphs.
Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems.
Explaining and exploring job recommendations: a user-driven approach for interacting with knowledge-based job recommender systems.
Users in the loop: a psychologically-informed approach to similar item retrieval.
Recommending what video to watch next: a multitask ranking system.
Deep generative ranking for personalized recommendation.
From preference into decision making: modeling user interactions in recommender systems.
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation.
Online ranking combination.
Personalized re-ranking for recommendation.
Whose data traces, whose voices? Inequality in online participation and why it matters for recommendation systems research.
Rude awakenings from behaviourist dreams. Methodological integrity and the GDPR.
