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Being Confident about the Quality of the Predictions in Recommender Systems [chapter]

Sergio Cleger-Tamayo, Juan M. Fernández-Luna, Juan F. Huete, Nava Tintarev
2013 Lecture Notes in Computer Science  
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]

L. Ardissono, C. Gena, P. Torasso, F. Bellifemine, A. Chiarotto, A. Difino, B. Negro
2003 Lecture Notes in Computer Science  
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

Dinkar Jadhav, Prof. Satpute B.S.
2016 IJARCCE  
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

Jacob Solomon
2014 Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14  
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]

Liliana Ardissono, Cristina Gena, Pietro Torasso, Fabio Bellifemine, Angelo Difino, Barbara Negro
2004 Personalized Digital Television  
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]

Manos Papagelis, Dimitris Plexousakis, Themistoklis Kutsuras
2005 Lecture Notes in Computer Science  
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

Shreya Agrawal, Pooja Jain
2017 International Journal of Computer Applications  
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?

Juliana Carvalho Ferreira, Cecilia Maria Patino
2019 Jornal Brasileiro de Pneumologia  
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

Guibing Guo, Jie Zhang, Daniel Thalmann, Neil Yorke-Smith
2014 Electronic Commerce Research and Applications  
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"

Larissa Chazette, Wasja Brunotte, Timo Speith
2021 Zenodo  
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

Tural Gurbanov, Francesco Ricci
2017 Italian Information Retrieval Workshop  
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

Mohammad Aamir, Mamta Bhusry
2015 International Journal of Computer Applications  
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?

Vinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, Joseph A. Konstan
2008 Proceedings of the 2008 ACM conference on Recommender systems - RecSys '08  
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

Juan G. Diaz Ochoa, Orsolya Csiszár, Thomas Schimper
2021 BMC Medical Informatics and Decision Making  
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

Fatih Aksel, Aysenur Birtürk
2010 ACM Conference on Recommender Systems  
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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