recsys 2018 论文列表
Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2-7, 2018.
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CHAMELEON: a deep learning meta-architecture for news recommender systems.
Beyond the top-N: algorithms that generate recommendations for self-actualization.
Video recommendation using crowdsourced time-sync comments.
Using textual summaries to describe a set of products.
SeRenA: a semantic recommender for all.
Towards the next generation of multi-criteria recommender systems.
Comparing recommender systems using synthetic data.
Testing a recommender system for self-actualization.
Mixed methods for evaluating user satisfaction.
Sequence-aware recommendation.
Multimedia recommender systems.
Emotions and personality in recommender systems: tutorial.
Modularizing deep neural network-inspired recommendation algorithms.
Concept to code: learning distributed representation of heterogeneous sources for recommendation.
ACM recsys'18 late-breaking results (posters).
Recsys challenge 2018: automatic music playlist continuation.
ACM recsys workshop on recommenders in tourism (rectour 2018).
The 2nd workshop on intelligent recommender systems by knowledge transfer & learning (recsysKTL).
Knowledge-aware and conversational recommender systems.
Recsys'18 joint workshop on interfaces and human decision making for recommender systems.
Third international workshop on health recommender systems (healthrecsys 2018).
2nd FATREC workshop: responsible recommendation.
REVEAL 2018: offline evaluation for recommender systems.
DLRS 2018: third workshop on deep learning for recommender systems.
2nd workshop on recommendation in complex scenarios (complexrec 2018).
Cognitive company discovery.
Picture-based navigation for diagnosing post-harvest diseases of apple.
Towards an open, collaborative REST API for recommender systems.
Query-based simple and scalable recommender systems with apache hivemall.
Automating recommender systems experimentation with librec-auto.
Module advisor: a hybrid recommender system for elective module exploration.
Tourrec: a tourist trip recommender system for individuals and groups.
Case recommender: a flexible and extensible python framework for recommender systems.
Extra: explaining team recommendation in networks.
Connecting sellers and buyers on the world's largest inventory.
Conversational content discovery via comcast X1 voice interface.
Artwork personalization at netflix.
Building recommender systems with strict privacy boundaries.
Measuring operational quality of recommendations: industry talk abstract.
Learning content and usage factors simultaneously to reduce clickbaits.
Hybrid search: incorporating contextual signals in recommendations at pinterest.
Hulu video recommendation: from relevance to reasoning.
Adapting session based recommendation for features through transfer learning.
Variational learning to rank (VL2R).
User preference learning in multi-criteria recommendations using stacked auto encoders.
CLoSe: Contextualized Location Sequence Recommender.
Semantic-based tag recommendation in scientific bookmarking systems.
Efficient online recommendation via low-rank ensemble sampling.
Audio-visual encoding of multimedia content for enhancing movie recommendations.
Rank and rate: multi-task learning for recommender systems.
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence.
A probabilistic model for intrusive recommendation assessment.
Kernelized probabilistic matrix factorization for collaborative filtering: exploiting projected user and item graph.
Learning within-session budgets from browsing trajectories.
Learning to recommend diverse items over implicit feedback on PANDOR.
Decomposing fit semantics for product size recommendation in metric spaces.
Attentive neural architecture incorporating song features for music recommendation.
Field-aware probabilistic embedding neural network for CTR prediction.
Learning consumer and producer embeddings for user-generated content recommendation.
Harnessing a generalised user behaviour model for next-POI recommendation.
Psrec: social recommendation with pseudo ratings.
A hierarchical bayesian model for size recommendation in fashion.
Deep neural network marketplace recommenders in online experiments.
Large-scale recommendation for portfolio optimization.
A crowdsourcing triage algorithm for geopolitical event forecasting.
RecGAN: recurrent generative adversarial networks for recommendation systems.
Measuring anti-relevance: a study on when recommendation algorithms produce bad suggestions.
Using citation-context to reduce topic drifting on pure citation-based recommendation.
CF4CF: recommending collaborative filtering algorithms using collaborative filtering.
Word2vec applied to recommendation: hyperparameters matter.
Recommendations for chemists: a case study.
Adaptive collaborative topic modeling for online recommendation.
Eliciting pairwise preferences in recommender systems.
Categorical-attributes-based item classification for recommender systems.
Spectral collaborative filtering.
User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms.
Recurrent knowledge graph embedding for effective recommendation.
Judging similarity: a user-centric study of related item recommendations.
Unbiased offline recommender evaluation for missing-not-at-random implicit feedback.
A field study of related video recommendations: newest, most similar, or most relevant?
Streamingrec: a framework for benchmarking stream-based news recommenders.
On the robustness and discriminative power of information retrieval metrics for top-N recommendation.
Get me the best: predicting best answerers in community question answering sites.
Exploring author gender in book rating and recommendation.
Enhancing structural diversity in social networks by recommending weak ties.
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility.
What's going on in my city?: recommender systems and electronic participatory budgeting.
Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendations.
Recommending social-interactive games for adults with autism spectrum disorders (ASD).
The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games.
Deep inventory time translation to improve recommendations for real-world retail.
Understanding user interactions with podcast recommendations delivered via voice.
Comfride: a smartphone based system for comfortable public transport recommendation.
Preference elicitation as an optimization problem.
No more ready-made deals: constructive recommendation for telco service bundling.
Calibrated recommendations.
Generation meets recommendation: proposing novel items for groups of users.
HOP-rec: high-order proximity for implicit recommendation.
Optimally balancing receiver and recommended users' importance in reciprocal recommender systems.
Interactive recommendation via deep neural memory augmented contextual bandits.
Neural gaussian mixture model for review-based rating prediction.
Causal embeddings for recommendation.
Deep reinforcement learning for page-wise recommendations.
Item recommendation on monotonic behavior chains.
Quality-aware neural complementary item recommendation.
Exploring recommendations under user-controlled data filtering.
Translation-based factorization machines for sequential recommendation.
Multistakeholder recommendation with provider constraints.
Impact of item consumption on assessment of recommendations in user studies.
Interpreting user inaction in recommender systems.
Explore, exploit, and explain: personalizing explainable recommendations with bandits.
Providing explanations for recommendations in reciprocal environments.
Effects of personal characteristics on music recommender systems with different levels of controllability.
Why I like it: multi-task learning for recommendation and explanation.
Recommending social cohesion.
Scalable structured prediction for richly structured socio-behavioral data.
Five E's: reflecting on the design of recommendations.
