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
Novel Perspectives in Collaborative Filtering 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 existingExpand
Novel Perspectives in Collaborative Filtering Recommender Systems
TLDR
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. Expand
On discovering non-obvious recommendations: using unexpectedness and neighborhood selection methods in collaborative filtering systems
TLDR
A new probabilistic neighborhood-based method is proposed as an improvement of the standard $k$-nearest neighbors approach, alleviating some of the most common problems of collaborative filtering recommender systems, based on classical metrics of dispersion and diversity as well as some newly proposed metrics. Expand
Moving beyond linearity and independence in top-N recommender systems
TLDR
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. Expand
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems
TLDR
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
On Unexpectedness in Recommender Systems
TLDR
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. Expand
DiABlO: Optimization based design for improving diversity in recommender system
TLDR
A single stage optimization based solution to achieve high diversity while maintaining requisite levels of accuracy is proposed and the superiority of the model over existing state of the art techniques is demonstrated by the results of experiments conducted on real world movie database. Expand
Cross-domain item recommendation based on user similarity
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. Expand
What recommenders recommend: an analysis of recommendation biases and possible countermeasures
TLDR
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
Probabilistic Neighborhood Selection in Collaborative Filtering Systems
TLDR
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
...
1
2
3
...

References

SHOWING 1-10 OF 24 REFERENCES
Being accurate is not enough: how accuracy metrics have hurt recommender systems
TLDR
This paper proposes informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies, and proposes new user-centric directions for evaluating recommender systems. Expand
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems
TLDR
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
On Unexpectedness in Recommender Systems: Or How to Expect the Unexpected
TLDR
A model to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics is proposed and a new concept of unexpectedness is proposed as recommending to users those items that depart from what they expect from the system. Expand
On Unexpectedness in Recommender Systems
TLDR
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. Expand
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three mainExpand
Looking for "Good" Recommendations: A Comparative Evaluation of Recommender Systems
TLDR
An empirical study that involved 210 users and considered seven RSs on the same dataset that use different baseline and state-of-the-art recommendation algorithms was discussed, measuring the user's perceived quality of each of them, focusing on accuracy and novelty of recommended items, and on overall users' satisfaction. Expand
Recommender systems: from algorithms to user experience
TLDR
It is argued that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and additional measures that have proven effective are suggested. Expand
Recommender Systems - An Introduction
TLDR
An overview of approaches to developing state-of-the-art recommender systems, including current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. Expand
Probabilistic Neighborhood Selection in Collaborative Filtering Systems
TLDR
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
Mixing it up: recommending collections of items
TLDR
This work introduces the idea of collection recommender systems and describes a design space for them including 3 main aspects that contribute to the overall value of a collection: the value of the individual items, co-occurrence interaction effects, and order effects including placement and arrangement of items. Expand
...
1
2
3
...