Abstract
Recommendation Systems have found extensive use in today’s web environment as they improve the overall user experience by providing users with personalized suggestions. Along with the traditional techniques like Collaborative and Content-based filtering, researchers have explored computational intelligence techniques to improve the performance of recommendation systems. In this paper, a similar approach has been taken in the form of applying a heuristic based technique on recommendation systems. The paper proposes a recommendation system based on a less explored nature-inspired technique called Gravitational Search Algorithm. The performance of this system is compared with that of a system using Particle Swarm Optimisation, which is a similar optimisation technique. The results show that Gravitational Search Algorithm excels in improving the accuracy of the recommendation model and also surpasses the model using Particle Swarm Optimization.
The original version of this chapter was revised. A few errors in the equations on pages 600 and 601 were corrected. The erratum to this chapter is available at 10.1007/978-3-319-61833-3_67
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Choudhary, V., Mullick, D., Nagpal, S. (2017). Gravitational Search Algorithm in Recommendation Systems. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_63
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