recsys 2016 论文列表
Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016.
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Recommender Systems from an Industrial and Ethical Perspective.
Proactive Recommendation Delivery.
Personalized Support for Healthy Nutrition Decisions.
Mining Information for the Cold-Item Problem.
Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources.
Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by Startups.
Generating Pseudotransactions for Improving Sparse Matrix Factorization.
Context-Based IDE Command Recommender System.
Tutorial: Lessons Learned from Building Real-life Recommender Systems.
People Recommendation Tutorial.
Matrix and Tensor Decomposition in Recommender Systems.
Group Recommender Systems.
RecSys Challenge 2016: Job Recommendations.
3rd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2016).
LSRS'16: Workshop on Large-Scale Recommender Systems.
Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016).
RecTour 2016: Workshop on Recommenders in Tourism.
RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS).
RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems.
RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendations.
Engendering Health with Recommender Systems.
4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE).
T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems Conference.
Topical Semantic Recommendations for Auteur Films.
RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information Network.
Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences.
Conversational Recommendation System with Unsupervised Learning.
A Recommender System to tackle Enterprise Collaboration.
Item-to-item Recommendations at Pinterest.
Multi-corpus Personalized Recommendations on Google Play.
Recommending the World's Knowledge: Application of Recommender Systems at Quora.
Hypothesis Testing: How to Eliminate Ideas as Soon as Possible.
Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business Value.
A Cross-Industry Machine Learning Framework with Explicit Representations.
Considering Supplier Relations and Monetization in Designing Recommendation Systems.
Feature Selection For Human Recommenders.
The Exploit-Explore Dilemma in Music Recommendation.
Recommending for the World.
Music Personalization at Spotify.
Marsbot: Building a Personal Assistant.
News Recommendations at scale at Bloomberg Media: Challenges and Approaches.
When Recommendation Systems Go Bad.
Mendeley: Recommendations for Researchers.
Bayesian Personalized Ranking with Multi-Channel User Feedback.
Recommending Repeat Purchases using Product Segment Statistics.
Bayesian Low-Rank Determinantal Point Processes.
Using Navigation to Improve Recommendations in Real-Time.
Efficient Bayesian Methods for Graph-based Recommendation.
Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach.
Representation Learning for Homophilic Preferences.
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation.
STAR: Semiring Trust Inference for Trust-Aware Social Recommenders.
Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks.
Recommending New Items to Ephemeral Groups Using Contextual User Influence.
MAPS: A Multi Aspect Personalized POI Recommender System.
Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems.
Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks.
Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling.
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation.
Discovering What You're Known For: A Contextual Poisson Factorization Approach.
The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems.
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations.
Convolutional Matrix Factorization for Document Context-Aware Recommendation.
Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation.
Behaviorism is Not Enough: Better Recommendations through Listening to Users.
Algorithms Aside: Recommendation As The Lens Of Life.
Past, Present, and Future of Recommender Systems: An Industry Perspective.
Recommender Systems with Personality.
A Package Recommendation Framework for Trip Planning Activities.
Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion.
Deep Neural Networks for YouTube Recommendations.
Domain-Aware Grade Prediction and Top-n Course Recommendation.
Crowd-Based Personalized Natural Language Explanations for Recommendations.
Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing.
Mechanism Design for Personalized Recommender Systems.
The Value of Online Customer Reviews.
ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud.
Observing Group Decision Making Processes.
Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques.
Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens.
Gaze Prediction for Recommender Systems.
Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models.
HCI for Recommender Systems: the Past, the Present and the Future.
Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback.
Addressing Cold Start for Next-song Recommendation.
Ask the GRU: Multi-task Learning for Deep Text Recommendations.
Latent Factor Representations for Cold-Start Video Recommendation.
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks.
Joint User Modeling across Aligned Heterogeneous Sites.
Query-based Music Recommendations via Preference Embedding.
Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization.
Local Item-Item Models For Top-N Recommendation.
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence.
Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization.
Field-aware Factorization Machines for CTR Prediction.
Intent-Aware Diversification Using a Constrained PLSA.
Adaptive, Personalized Diversity for Visual Discovery.
Contrasting Offline and Online Results when Evaluating Recommendation Algorithms.
Multi-Word Generative Query Recommendation Using Topic Modeling.
A Scalable Approach for Periodical Personalized Recommendations.
A Coverage-Based Approach to Recommendation Diversity On Similarity Graph.
Recommender Systems for Self-Actualization.
Recommendations with a Purpose.
Personalization for Google Now: User Understanding and Application to Information Recommendation and Exploration.
Automated Machine Learning in the Wild.
