A cost model for similarity queries in metric spaces
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Web-collaborative filtering: recommending music by crawling the Web
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
A practical query-by-humming system for a large music database
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Automated extraction of music snippets
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
A music recommender based on audio features
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
An industrial-strength content-based music recommendation system
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Inferring similarity between music objects with application to playlist generation
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Query expansion for hash-based image object retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A fuzzy framework for defining dynamic playlist generation heuristics
Fuzzy Sets and Systems
Music recommendation based on acoustic features and user access patterns
IEEE Transactions on Audio, Speech, and Language Processing
A novel music recommender by discovering preferable perceptual-patterns from music pieces
Proceedings of the 2010 ACM Symposium on Applied Computing
Relevant shape contour snippet extraction with metadata supported hidden Markov models
Proceedings of the ACM International Conference on Image and Video Retrieval
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Using rich social media information for music recommendation via hypergraph model
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
A fast audio similarity retrieval method for millions of music tracks
Multimedia Tools and Applications
Local and global scaling reduce hubs in space
The Journal of Machine Learning Research
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The growth of music resources on personal devices and Internet radio has increased the need for music recommendations. In this paper, aiming at providing an efficient and general solution, we present a search-based solution for scalable music recommendations. In this solution a music piece is first transformed to a music signature sequence in which each signature characterizes the timbre of a local music clip. Based on such signatures, a scale-sensitive method is then proposed to index the music pieces for similarity search, using the locality sensitive hashing (LSH). The scale-sensitive method can numerically find the appropriate parameters for indexing various scales of music collections, and thus can guarantee a proper number of nearest neighbors are found in search. In the recommendation stage, representative signatures from snippets of a seed piece are extracted as query terms, to retrieve pieces with similar melodies for suggestions. We also design a relevance-ranking function to sort the search results, based on the criteria that include matching ratio, temporal order, term weight, and matching confidence. Finally, with the search results, we propose a strategy to generate a dynamic playlist which can automatically expand with time. Evaluations of several music collections at various scales showed that our approach achieves encouraging results in terms of recommendation satisfaction and system scalability.