Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
User-defined music sequence retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Combinatorial Approach to Content-Based Music Selection
IEEE MultiMedia
Ambient Intelligence, Wireless Networking, And Ubiquitous Computing
Ambient Intelligence, Wireless Networking, And Ubiquitous Computing
Creating collections with automatic suggestions and example-based refinement
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
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We present the design of an algorithm for use in an interactive music system that automatically generates music playlists that fit the music preferences of a user. To this end, we introduce a formal model, define the problem of automatic playlist generation (APG), and prove its NP-hardness. We use a local search (LS) procedure employing a heuristic improvement to standard simulated annealing (SA) to solve the APG problem. In order to employ this LS procedure, we introduce an optimization variant of the APG problem, which includes the definition of penalty functions and a neighborhood structure. To improve upon the performance of the standard SA algorithm, we incorporated three heuristics referred to as song domain reduction, partial constraint voting, and a two-level neighborhood structure. We evaluate the developed algorithm by comparing it to a previously developed approach based on constraint satisfaction (CS), both in terms of run time performance and quality of the solutions. For the latter we not only considered the penalty of the resulting solutions, but we also performed a conclusive user evaluation to assess the subjective quality of the playlists generated by both algorithms. In all tests, the LS algorithm was shown to be a dramatic improvement over the CS algorithm.