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 12 October 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… 
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30 Citations
Highly Influential Citations
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30 Citations

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Novel Perspectives in Collaborative Filtering Recommender Systems
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The overall goal of this research program is to move the focus from even more accurate rating predictions to a holistic experience to the users by avoiding the over-specialization and concentration problems and providing the users with non-obvious but high quality personalized recommendations that they will remarkably like.
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This paper focuses on the development of methods capturing higher-order relations between the items, cross-feature interactions and intra-set dependencies which can potentially lead to a considerable enhancement of the recommendation accuracy.
On Unexpectedness in Recommender Systems
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A method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics is proposed, which outperforms baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.
DiABlO: Optimization based design for improving diversity in recommender system
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TLDR
A cross-domain item recommendation model based on user similarity called CRUS is proposed, which firstly introduces the trust relation among friends into cross- domain recommendation and outperforms the baseline methods on MAE and RMSE.
What recommenders recommend: an analysis of recommendation biases and possible countermeasures
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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.
Probabilistic Neighborhood Selection in Collaborative Filtering Systems
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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.
On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings
TLDR
The proposed algorithm, which is termed as CFVR, has been extensively evaluated and the results show that it achieves to considerably improve the capability of a collaborative filtering system to formulate tailored recommendations for each user, when operating on sparse datasets, while at the same time improves rating prediction quality.
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A method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics is proposed, which outperforms baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.
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