Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
A Parallel Implementation of the Simplex Function Minimization Routine
Computational Economics
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
From hits to niches?: or how popular artists can bias music recommendation and discovery
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
MusicBox: personalized music recommendation based on cubic analysis of social tags
IEEE Transactions on Audio, Speech, and Language Processing
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender Systems Handbook
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
The million song dataset challenge
Proceedings of the 21st international conference companion on World Wide Web
Supercharging recommender systems using taxonomies for learning user purchase behavior
Proceedings of the VLDB Endowment
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Spotting trends: the wisdom of the few
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Efficient retrieval of recommendations in a matrix factorization framework
Proceedings of the 21st ACM international conference on Information and knowledge management
A live comparison of methods for personalized article recommendation at forbes.com
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Latent factor models with additive and hierarchically-smoothed user preferences
Proceedings of the sixth ACM international conference on Web search and data mining
Modeling user's receptiveness over time for recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Measuring spontaneous devaluations in user preferences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection
Proceedings of the 7th ACM conference on Recommender systems
Personalized next-song recommendation in online karaokes
Proceedings of the 7th ACM conference on Recommender systems
Lazy paired hyper-parameter tuning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
Robust multivariate autoregression for anomaly detection in dynamic product ratings
Proceedings of the 23rd international conference on World wide web
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
Hi-index | 0.00 |
In the past decade large scale recommendation datasets were published and extensively studied. In this work we describe a detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models. The Yahoo! Music dataset consists of more than a million users, 600 thousand musical items and more than 250 million ratings, collected over a decade. It is characterized by three unique features: First, rated items are multi-typed, including tracks, albums, artists and genres; Second, items are arranged within a four level taxonomy, proving itself effective in coping with a severe sparsity problem that originates from the unusually large number of items (compared to, e.g., movie ratings datasets). Finally, fine resolution timestamps associated with the ratings enable a comprehensive temporal and session analysis. We further present a matrix factorization model exploiting the special characteristics of this dataset. In particular, the model incorporates a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings. To gain additional insights of its properties, we organized the KddCup-2011 competition about this dataset. As the competition drew thousands of participants, we expect the dataset to attract considerable research activity in the future.