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SemStim at the LOD-RecSys 2014 Challenge

  • Benjamin Heitmann
  • Conor Hayes
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)

Abstract

SemStim is a graph-based recommendation algorithm which is based on Spreading Activation and adds targeted activation and duration constraints. SemStim is not affected by data sparsity, the cold-start problem or data quality issues beyond the linking of items to DBpedia. The overall results show that the performance of SemStim for the diversity task of the challenge is comparable to the other participants, as it took 3rd place out of 12 participants with 0.0413 F1@20 and 0.476 ILD@20. In addition, as SemStim has been designed for the requirements of cross-domain recommendations with different target and source domains, this shows that SemStim can also provide competitive single-domain recommendations.

Keywords

User Profile Target Domain Activation Threshold Spreading Activation Source Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.INSIGHT @ NUI GalwayNational University of Ireland, GalwayGalwayIreland

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