A Survey of Techniques for Developing Recommendation Systems


The amount information handled by information systems is growing steadily. This requires the use of recommendation systems for handling all this information. A recommendation system helps to find items of interest, but the development of this kind of systems is complicated due to the existence of a large number of algorithms for recommendation systems. In this context, this paper proposes an analysis of the algorithms used in recommendation systems and its most common application domain. The algorithms to calculate similarity presented in this paper are the algorithm of the cosine and Pearson correlation. The clustering algorithms analyzed are K-nearest neighbors (KNN) and clustering. All these algorithms are used in the collaborative filtering. The algorithms used for content-based filtering and hybrid approaches are Naive Bayes Classifier, Floyd-Warshall algorithm, Demographic recomm

  • Abstract
  • Keywords
  • Introduction
  • Collaborative Filtering Algorithms for Developing Recommendation Systems.
  • Content-Based Algorithms for Developing Recommendation Systems
  • Other Approaches for Developing Recommendation Systems.
  • Indicators to Measure Recommendation Systems
  • Conclusions
  • Acknowledgements
  • References

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In