Beyond rating prediction accuracy: on new perspectives in recommender systems

@article{Adamopoulos2013BeyondRP,
  title={Beyond rating prediction accuracy: on new perspectives in recommender systems},
  author={Panagiotis Adamopoulos},
  journal={Proceedings of the 7th ACM conference on Recommender systems},
  year={2013}
}
  • Panagiotis Adamopoulos
  • Published 2013
  • Computer Science
  • Proceedings of the 7th ACM conference on Recommender systems
This paper proposes a number of studies in order to move recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored paradigms and also propose new approaches aiming at more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. In particular, we move our focus from even more… Expand
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29 Citations
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29 Citations

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Citation Type

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It is shown that popular recommendation techniques—despite often being similar when compared with the help of accuracy measures—can be quite different with respect to which items they recommend. Expand
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An empirical study shows that the proposed probabilistic method alleviates the over-specialization and concentration biases in common recommender systems by generating recommendation lists that are very different from the classical collaborative filtering approach and also increasing the aggregate diversity and mobility of recommendations. Expand
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References

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Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems
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An empirical study shows that the proposed novel method for estimating unknown ratings and recommendation opportunities outperforms the standard user-based collaborative filtering approach by a wide margin in terms of item prediction accuracy and utility-based ranking metrics across various experimental settings. Expand
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