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Being Confident about the Quality of the Predictions in Recommender Systems
[chapter]
2013
Lecture Notes in Computer Science
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users to the fact that the system might be wrong. ...
In particular, it was found that unrated items have lower confidence compared to the entire item set -highlighting the importance of explanations for novel but risky recommendations. ...
This paper has been supported by the Spanish "Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía", the "Ministerio de Ciencia e Innovación" and the research programme "Consolider Ingenio ...
doi:10.1007/978-3-642-36973-5_35
fatcat:5zsfbwleaferdk4lz67ytc7jyy
Personalized Recommendation of TV Programs
[chapter]
2003
Lecture Notes in Computer Science
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This paper presents the user modeling and recommendation techniques applied in Personal Program Guide (PPG), a system generating personalized Electronic Program Guides for digital TV. ...
The PPG recommends TV programs by relying on the integration of heterogeneous user modeling techniques. * This work has been partially supported by the Italian M.I.U.R. ...
The formula merges the predictions in a weighted way, on the basis of the experts' confidence, in order to privilege estimates based on higher quality information about the user. ...
doi:10.1007/978-3-540-39853-0_39
fatcat:sgbaghtaizbnxgz7xitq4dwnwq
Survey on Quality of Service Recommendation
2016
IJARCCE
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Sometimes this information may be having problem to recommend user about product recommendation because of the lack of reviews and rating of particular product. ...
To provide quality of service to the recommendation have to improve overall evaluation of rating. Find the trust of the user service rating and feature. ...
In this system, we propose framework to provide quality of ...
doi:10.17148/ijarcce.2016.51275
fatcat:x3pbmraowbdv5jvgnurycumdj4
Customization bias in decision support systems
2014
Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14
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I also show that this customization bias is the result of using a DSS to seek confirmatory information in a recommendation. ...
Many Decision Support Systems (DSS) afford customization of inputs or algorithms before generating recommendations to a decision maker. ...
This work was supported with funding from the College of Communication Arts & Sciences and the Graduate School of Michigan State University. ...
doi:10.1145/2556288.2557211
dblp:conf/chi/Solomon14
fatcat:2z2lrungavcr3lx7zvststcbpy
User Modeling and Recommendation Techniques for Personalized Electronic Program Guides
[chapter]
2004
Personalized Digital Television
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This chapter presents the recommendation techniques applied in Personal Program Guide (PPG). This is a system generating personalized Electronic Program Guides for Digital TV. ...
This model results from the integration of different preference acquisition modules that handle explicit user preferences, stereotypical information about TV viewers, and information about the user's viewing ...
Project (Technology System for Cultural Heritage in Tourism). We are grateful to Flavio Portis, who helped us in the development of the Stereotypical UM Expert of the PPG. ...
doi:10.1007/1-4020-2164-x_1
fatcat:aa2w6ypemfgvlklltxij43e5si
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences
[chapter]
2005
Lecture Notes in Computer Science
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In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. ...
Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as "similar" according to their logged history of prior transactions ...
Recommendation Quality Recommendation systems employ efficient prediction algorithms for providing accurate recommendations to users. ...
doi:10.1007/11429760_16
fatcat:5p4pb762e5fmjeurguk4gth4xm
About Performance Evaluation of the Movie Recommendation Systems
2017
International Journal of Computer Applications
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For more and more usage of any system, it is necessary to know about the efficiency of the system and for this reason performance evaluation of a Recommendation system is done. ...
By doing the performance evaluation of a system, one can prove the potential of a recommendation system. The more high performance a system gives more is its worth as compared to others. ...
So to handle the problem of scalability, algorithm requires to be optimized.
Confidence [3] Confidence refers to the recommendation system's faith in the predictions provided. ...
doi:10.5120/ijca2017912739
fatcat:dthbja57fratxlaapsf3gzgafi
Clinical practice guidelines: how do they help clinicians and patients make important decisions about health?
2019
Jornal Brasileiro de Pneumologia
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The CPG classified this recommendation as "strong" and with "moderate confidence in effect estimates". (1) ...
In 2017, a clinical practice guideline (CPG) about the use of mechanical ventilation in adult patients with acute respiratory distress syndrome (ARDS), sponsored by three medical societies, recommended ...
The strength of a recommendation reflects the extent to which one can be confident that the desirable effects of an intervention outweigh undesirable effects. ...
doi:10.1590/1806-3713/e20190321
pmid:31618293
fatcat:65mlcerbsnfnjnbpj6zdyfv2qe
Leveraging prior ratings for recommender systems in e-commerce
2014
Electronic Commerce Research and Applications
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User ratings are the essence of recommender systems in e-commerce. ...
A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments ...
Neil Yorke-Smith thanks the Operations group at the Judge Business School and the fellowship at St Edmunds College, Cambridge. ...
doi:10.1016/j.elerap.2014.10.003
fatcat:ppdi6bfr7bhvfixh4dd4usknwe
Supplementary Material for Research Paper "Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue"
2021
Zenodo
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In particular, more details on the Systematic Literature Review (SLR) – e.g., inclusion/exclusion criteria, complete list of analyzed papers, codes, etc. – and our workshops can be found here. ...
This is the supplementary material for the research paper Exploring Explainability: A Definition, a Model, and a Knowledge Catalogue accepted at the IEEE 29th Requirements Engineering Conference. ...
higher confidence in system recommendations higher degrees of confidence improve confidence of the decision quality increase confidence in system's abilities increase reliance increase the end user's ...
doi:10.5281/zenodo.5114922
fatcat:4os3nvqzlvek5kgnyoacj2khxm
Exploiting Multiple Action Types in Recommender Systems
2017
Italian Information Retrieval Workshop
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This paper presents an ongoing research in the field of implicit feedback recommender systems employing information about multiple action types. ...
Implicit feedback recommender systems provide personalized suggestions for items that are predicted to be of interest to the user, by collecting online users' activity and inferring from it users' preferences ...
The recommender is not only a channel for delivering recommendations. In fact, recommendations generate reactions and tailor-made recommendations can be used to acquire the most informative feedback. ...
dblp:conf/iir/Gurbanov017
fatcat:vlazlx5pvrdohm7bwb7aaamjfe
Recommendation System: State of the Art Approach
2015
International Journal of Computer Applications
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Certainly, recommendation systems have an assortment of properties that may entail experiences of user such as user preference, prediction accuracy, confidence, trust, etc. ...
In this paper we present a categorical reassess of the field of recommender systems and Approaches for Evaluation of Recommendation System to propose the recommendation method that would further help to ...
Confidence The level of systems trust in its recommendation or prediction is known as confidence in the recommendation system. Confidence in the predicted property rises as the amount of data rises. ...
doi:10.5120/21281-4200
fatcat:bpkrrywowrfjfhot45jlo5ps6u
Who predicts better?
2008
Proceedings of the 2008 ACM conference on Recommender systems - RecSys '08
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Algorithmic recommender systems attempt to predict which items a target user will like based on information about the user's prior preferences and the preferences of a larger community. ...
We compare the performance of MovieLens algorithmic predictions with the recommendations made, based on the same user profiles, by active MovieLens users. ...
ACKNOWLEDGEMENTS We appreciate that the idea for this project came out of a panel session at the ACM Recommender Systems Conference. We thank the participants who stimulated this research. ...
doi:10.1145/1454008.1454042
dblp:conf/recsys/KrishnanNNDK08
fatcat:ll3il7leh5ffhoji3drhb3vwxu
Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks
2021
BMC Medical Informatics and Decision Making
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In this way, we provide a recommendation of the best possible therapy based on the outcome of the model and the confidence of this recommendation when the outcome of model 1 is compared with the outcome ...
Our methodology can be extended to different clinical scenarios where recommender systems can be applied. ...
Acknowledgements We are very grateful for the discussions and contribution of Martin Grundman in the conceptual development of the recommendation system, Gábor Csiszár and Elena Ramirez for critical proofreading ...
doi:10.1186/s12911-021-01553-3
pmid:34112161
fatcat:3pzcxxrh6nbevdeywr3giimcmm
Enhancing Accuracy of Hybrid Recommender Systems through Adapting the Domain Trends
2010
ACM Conference on Recommender Systems
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In this paper, we propose an adaptive method for hybrid recommender systems, in which the combination of algorithms are learned and dynamically updated from the results of previous predictions. ...
Hybrid recommender systems combine several algorithms based on their hybridization strategy. Prediction algorithm selection strategy directly influence the accuracy of the hybrid recommenders. ...
It can be inferred from chart that, the prediction quality of the adaptive system performs better than the baseline. ...
dblp:conf/recsys/AkselB10
fatcat:sekiny7jsrczphnqxbgejzfq3a
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