Ujjin, S and Bentley, PJ (2003) Particle swarm optimization recommender system. In: PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03). (pp. 124 - 131). IEEE
Full text not available from this repository.
Recommender systems are new types of internet-based software tools, designed to help users rind their way through today's complex on-line shops and entertainment websites. This paper describes a new recommender system, which employs a particle swarm optimization (PSO) algorithm to learn personal preferences of users and provide tailored suggestions. Experiments are carried out to observe the performance of the system and results are compared to those obtained from the genetic algorithm (GA) recommender system I I I and a standard, non-adaptive system based on the Pearson algorithm .
|Title:||Particle swarm optimization recommender system|
|Event:||IEEE Swarm Intelligence Symposium|
|Dates:||2003-04-24 - 2003-04-26|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
Archive Staff Only: edit this record