Flexible recommendation using random walks on implicit feedback graph
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
The need for music information retrieval with user-centered and multimodal strategies
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Context-aware music recommender systems: workshop keynote abstract
Proceedings of the 21st international conference companion on World Wide Web
Mining microblogs to infer music artist similarity and cultural listening patterns
Proceedings of the 21st international conference companion on World Wide Web
The million song dataset challenge
Proceedings of the 21st international conference companion on World Wide Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
Local implicit feedback mining for music recommendation
Proceedings of the sixth ACM conference on Recommender systems
Swarming to rank for recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Inferring personal traits from music listening history
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Being picky: processing top-k queries with set-defined selections
Proceedings of the 21st ACM international conference on Information and knowledge management
A comparative study of heterogeneous item recommendations in social systems
Information Sciences: an International Journal
Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Preferential attachment in online networks: measurement and explanations
Proceedings of the 5th Annual ACM Web Science Conference
A hidden Markov model for collaborative filtering
MIS Quarterly
Measuring spontaneous devaluations in user preferences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Query by humming: Automatically building the database from music recordings
Pattern Recognition Letters
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With so much more music available these days, traditional ways of finding music have diminished. Today radio shows are often programmed by large corporations that create playlists drawn from a limited pool of tracks. Similarly, record stores have been replaced by big-box retailers that have ever-shrinking music departments. Instead of relying on DJs, record-store clerks or their friends for music recommendations, listeners are turning to machines to guide them to new music. In this book, scar Celma guides us through the world of automatic music recommendation. He describes how music recommenders work, explores some of the limitations seen in current recommenders, offers techniques for evaluating the effectiveness of music recommendations and demonstrates how to build effective recommenders by offering two real-world recommender examples. He emphasizes the user's perceived quality, rather than the system's predictive accuracy when providing recommendations, thus allowing users to discover new music by exploiting the long tail of popularity and promoting novel and relevant material ("non-obvious recommendations"). In order to reach out into the long tail, he needs to weave techniques from complex network analysis and music information retrieval. Aimed at final-year-undergraduate and graduate students working on recommender systems or music information retrieval, this book presents the state of the art of all the different techniques used to recommend items, focusing on the music domain as the underlying application.