@article{10.1145/2926720,
author = {Kaminskas, Marius and Bridge, Derek},
title = {{Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems}},
year = {2016},
volume = {7},
number = {1},
journal = {ACM Trans. Interact. Intell. Syst.}
}

@inproceedings{Chaney:2018:ACR:3240323.3240370,
 author = {Chaney, Allison J. B. and Stewart, Brandon M. and Engelhardt, Barbara E.},
 title = {{How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility}},
 booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems},
 year = {2018},
 pages = {224--232}
}

@inproceedings{Kohrs2001:smart_objects,
  author = {{K}ohrs, {A}rnd and  {M}{\'e}rialdo, {B}ernard},
  year = {2001},
  title = {{I}mproving Collaborative Filtering for New-Users by Smart Object Selection},
  booktitle = {Proceedings of the {I}nternational {C}onference on {M}edia {F}utures ({ICME})},
}

@inproceedings{Amatriain-Et-Al-2009,
author = {Amatriain, X. and Pujol, J.M. and Oliver, N.},
year = {2009},
title = {{I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems}},
booktitle = {Proceedings of the Seventeenth Conference on User Modeling, Adaptation, and Personalization}, 
pages = {247--258}
}

@article{herlocker2004evaluating,
  title={Evaluating Collaborative Filtering Recommender Systems},
  author={Herlocker, Jonathan L and Konstan, Joseph A and Terveen, Loren G and Riedl, John T},
  journal={ACM Transactions on Information Systems (TOIS)},
  volume={22},
  number={1},
  pages={5--53},
  year={2004},
  publisher={ACM New York, NY, USA}
}

@inproceedings{Zhang-Et-Al-2014,
author = {Zhang, Mi and Tang, Jie and Zhang, Xuchen and Xue, Xiangyang},
title = {{Addressing Cold Start in Recommender Systems: A Semi-Supervised Co-Training Algorithm}},
year = {2014},
booktitle = {Proceedings of the Thirty-seventh International ACM SIGIR Conference on Research \& Development in Information Retrieval},
pages = {73--82}
}

@inproceedings{daCosta-Et-Al-2018,
author = {da Costa, Arthur F. and Manzato, Marcelo G. and Campello, Ricardo J. G. B.},
title = {{CoRec: A Co-Training Approach for Recommender Systems}},
year = {2018},
booktitle = {Proceedings of the Thirty-third Annual ACM Symposium on Applied Computing},
pages = {696–703}
}

@article{Burke-2002,
author = {Burke, Robin},
title = {{Hybrid Recommender Systems: Survey and Experiments}},
year = {2002},
volume = {12},
number = {4},
journal = {User Modeling and User-Adapted Interaction},
pages = {331–-370}
}

@inproceedings{ning2011slim,
  title={Slim: Sparse Linear Methods for Top-N Recommender Systems},
  author={Ning, Xia and Karypis, George},
  booktitle={2011 IEEE 11th International Conference on Data Mining},
  pages={497--506},
  year={2011},
  organization={IEEE}
}

@inproceedings{rendle2009bpr,
  title={{BPR: Bayesian Personalized Ranking From Implicit Feedback}},
  author={Rendle, Steffen and Freudenthaler, Christoph and Gantner, Zeno and Schmidt-Thieme, Lars},
  booktitle={Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence},
  pages={452--461},
  year={2009}
}

@article{koren2009matrix,
  title={Matrix Factorization Techniques for Recommender Systems},
  author={Koren, Yehuda and Bell, Robert and Volinsky, Chris},
  journal={Computer},
  pages={30--37},
  year={2009},
  publisher={IEEE}
}

@inproceedings{hammer2007process,
  title={How to Process Uncertainty in Machine Learning?},
  author={Hammer, Barbara and Villmann, Thomas},
  booktitle={European Symposium on Artificial Neural Networks},
  pages={79--90},
  year={2007}
}

@inproceedings{gal2016dropout,
  title={{Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning}},
  author={Gal, Yarin and Ghahramani, Zoubin},
  booktitle={Proceedings of the Thirty-third International Conference on Machine Learning},
  pages={1050--1059},
  year={2016}
}

@article{belford2018stability,
  title={{Stability of Topic Modeling via Matrix Factorization}},
  author={Belford, Mark and Mac Namee, Brian and Greene, Derek},
  journal={Expert Systems With Applications},
  volume={91},
  pages={159--169},
  year={2018}
}

@article{adomavicius2011improving,
  title={Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques},
  author={Adomavicius, Gediminas and Kwon, YoungOk},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={24},
  number={5},
  pages={896--911},
  year={2011},
  publisher={IEEE}
}

@article{johnson2014logistic,
  title={Logistic Matrix Factorization for Implicit Feedback Data},
  author={Johnson, Christopher C.},
  journal={Advances in Neural Information Processing Systems},
  volume={27},
  year={2014}
}


@article{zenebe2009representation,
  title={{Representation, Similarity Measures and Aggregation Methods Using Fuzzy Sets for Content-Based Recommender Systems}},
  author={Zenebe, Azene and Norcio, Anthony F.},
  journal={Fuzzy Sets and Systems},
  volume={160},
  number={1},
  pages={76--94},
  year={2009}
}

@article{block1998exploring,
  title={Exploring Interpretations of Questionnaire Items},
  author={Block, David},
  journal={System},
  volume={26},
  number={3},
  pages={403--425},
  year={1998}
}

@inproceedings{o2006detecting,
  title={Detecting Noise in Recommender System Databases},
  author={O'Mahony, Michael P. and Hurley, Neil J. and Silvestre, Gu{\'e}nol{\'e} CM},
  booktitle={Proceedings of the 11th International Conference on Intelligent User Interfaces},
  pages={109--115},
  year={2006}
}


@article{mazurowski_estimating_2013,
	title = {{Estimating Confidence of Individual Rating Predictions in Collaborative Filtering Recommender Systems}},
	volume = {40},
	number = {10},
	journal = {Expert Systems With Applications},
	author = {Mazurowski, Maciej A.},
	year = {2013},
	pages = {3847--3857}
}

@article{zhang_prediction_2016,
	title = {Prediction Uncertainty in Collaborative Filtering: {Enhancing} Personalized Online Product Ranking},
	volume = {83},
	issn = {0167-9236},
	shorttitle = {Prediction uncertainty in collaborative filtering},
	url = {http://www.sciencedirect.com/science/article/pii/S0167923615002183},
	doi = {10.1016/j.dss.2015.12.004},
	abstract = {Personalized product ranking provides support to the decision making of online consumers and helps improve their satisfaction, since consumers always face a large volume of choices when they are shopping online. Recommender systems with collaborative filtering techniques are commonly used for this purpose, wherein products are ranked according to their predicted ratings. However, this kind of ranking approaches (namely, Ranking by Collaborative Filtering, RCF for short) have generally ignored the impacts of prediction uncertainty. This paper proposes a novel ranking approach called RPU (Ranking with Prediction Uncertainty), which utilizes posterior rating distribution and confidence level of prediction as two key factors for prediction uncertainty. Serving as a critical component of the generalized ranking framework, RPU aims to improve the accuracy of personalized product ranking through incorporating the uncertainty information. Experiments using real-world data of movie ratings show that RPU achieves higher ranking performance compared to traditional RCF and the results are robust in terms of sparse data.},
	language = {en},
	urldate = {2020-03-10},
	journal = {Decision Support Systems},
	author = {Zhang, Mingyue and Guo, Xunhua and Chen, Guoqing},
	month = mar,
	year = {2016},
	keywords = {Collaborative filtering, Confidence level, Online product ranking, Posterior rating distribution, Prediction uncertainty},
	pages = {10--21},
	annote = {Provide a full framework on how to make recommendations based on a pair (rating, uncertainty), uses empirical uncertainty measures},
	file = {ScienceDirect Full Text PDF:/home/vcoscrato/Zotero/storage/3YGE4WYM/Zhang et al. - 2016 - Prediction uncertainty in collaborative filtering.pdf:application/pdf;ScienceDirect Snapshot:/home/vcoscrato/Zotero/storage/757DRBIZ/S0167923615002183.html:text/html}
}

@article{bobadilla_reliability_2018,
	title = {{Reliability Quality Measures for Recommender Systems}},
	volume = {442},
	journal = {Information Sciences},
	author = {Bobadilla, Jesus and Gutiérrez, A. and Ortega, F. and Zhu, B.},
	year = {2018},
	pages = {145--157}
}

@article{zhu2018fuzzy,
  title={A Fuzzy Clustering-Based Denoising Model for Evaluating Uncertainty in Collaborative Filtering Recommender Systems},
  author={Zhu, Jun and Han, Lixin and Gou, Zhinan and Yuan, Xiaofeng},
  journal={Journal of the Association for Information Science and Technology},
  volume={69},
  number={9},
  pages={1109--1121},
  year={2018},
  publisher={Wiley Online Library}
}

@article{hernando_incorporating_2013,
	title = {Incorporating Reliability Measurements Into the Predictions of a Recommender System},
	volume = {218},
	issn = {00200255},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0020025512004434},
	doi = {10.1016/j.ins.2012.06.027},
	language = {en},
	urldate = {2020-04-16},
	journal = {Information Sciences},
	author = {Hernando, Antonio and Bobadilla, Jesús and Ortega, Fernando and Tejedor, Jorge},
	month = jan,
	year = {2013},
	pages = {1--16},
	annote = {This propose adding a list of empirical factors that compose a measure of uncertainty.},
	file = {Hernando et al. - 2013 - Incorporating reliability measurements into the pr.pdf:/home/vcoscrato/Zotero/storage/W8U8RTY8/Hernando et al. - 2013 - Incorporating reliability measurements into the pr.pdf:application/pdf}
}

@article{moradi_reliability-based_2015,
	title = {A Reliability-Based Recommendation Method to Improve Trust-Aware Recommender Systems},
	volume = {42},
	issn = {09574174},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0957417415003553},
	doi = {10.1016/j.eswa.2015.05.027},
	language = {en},
	number = {21},
	urldate = {2020-04-16},
	journal = {Expert Systems With Applications},
	author = {Moradi, Parham and Ahmadian, Sajad},
	month = nov,
	year = {2015},
	pages = {7386--7398},
	annote = {Creates a trust network and extract reliability measures from it that are used to improve itself. Uses the reliability measure proposed by "Incorporating reliability measurements..."},
	file = {Moradi and Ahmadian - 2015 - A reliability-based recommendation method to impro.pdf:/home/vcoscrato/Zotero/storage/LESZ7YKM/Moradi and Ahmadian - 2015 - A reliability-based recommendation method to impro.pdf:application/pdf}
}

@article{javari_recommender_2014,
	title = {Recommender Systems Based on Collaborative Filtering and Resource Allocation},
	volume = {4},
	issn = {1869-5469},
	url = {https://doi.org/10.1007/s13278-014-0234-0},
	doi = {10.1007/s13278-014-0234-0},
	abstract = {Recommendation systems are important part of electronic commerce, where appropriate items are recommended to potential users. The most common algorithms used for constructing recommender systems in commercial applications are collaborative filtering methods and their variants, which is mainly due to their simple implementation. In these methods, structural features of bipartite network of users and items are used and potential items are recommended to the users based on a similarity measure that shows how similar the behavior of the users is. Indeed, the performance of the memory-based CF algorithms heavily depends on the quality of similarities obtained among users/items. As the obtained similarities are more reliable, better performance for the recommender systems is expected. In this paper, we propose three models to extract reliability of similarities estimated in classic recommenders. We incorporate the obtained reliabilities to improve performance of the recommender systems. In the proposed algorithms for reliability extraction, a number of elements are taken into account including the structure of the user-item bipartite network, the individual profile of the users, i.e., how many items they have rated, and that of the items, i.e., how many users have rated them. Among the proposed methods, the method based on resource allocation provides the highest performance as compared to others. Our numerical results on two benchmark datasets (Movielens and Netflix) shows that employing resource allocation in classical recommenders significantly improves their performance. These results are of great importance since including resource allocation in the systems does not increase their computational complexity.},
	language = {en},
	number = {1},
	urldate = {2020-04-16},
	journal = {Social Network Analysis and Mining},
	author = {Javari, Amin and Gharibshah, Joobin and Jalili, Mahdi},
	month = oct,
	year = {2014},
	pages = {234},
	annote = {Calculate user similarity reliability. Weaking for example strong similarity determined by very popular items. Uses this to calculate a likelihood of existence of a link between non-connected nodes (resource allocation).},
	file = {Springer Full Text PDF:/home/vcoscrato/Zotero/storage/ZKUW7RBG/Javari et al. - 2014 - Recommender systems based on collaborative filteri.pdf:application/pdf}
}

@inproceedings{massa2007trust,
  title={Trust-Aware Recommender Systems},
  author={Massa, Paolo and Avesani, Paolo},
  booktitle={Proceedings of the 2007 ACM Conference on Recommender Systems},
  pages={17--24},
  year={2007}
}

@inproceedings{zhang2017modeling,
  title={Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations},
  author={Zhang, Xiaoying and Zhao, Junzhou and Lui, John CS},
  booktitle={Proceedings of the Eleventh ACM Conference on Recommender Systems},
  pages={98--106},
  year={2017}
}

@inproceedings{adomavicius2014biasing,
  title={De-Biasing User Preference Ratings in Recommender Systems},
  author={Adomavicius, Gediminas and Bockstedt, Jesse and Curley, Shawn and Zhang, Jingjing},
  booktitle={RecSys 2014 Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2014)},
  pages={2--9},
  year={2014}
}

@article{zhu2018assigning,
  title={Assigning Reliability Values to Recommendations Using Matrix Factorization},
  author={Zhu, Bo and Ortega, Fernando and Bobadilla, Jes{\'u}s and Guti{\'e}rrez, Abraham},
  journal={Journal of Computational Science},
  volume={26},
  pages={165--177},
  year={2018}
}

@inproceedings{adomavicius2007towards,
  title={{Towards More Confident Recommendations: Improving Recommender Systems Using Filtering Approach Based on Rating Variance}},
  author={Adomavicius, Gediminas and Kamireddy, Sreeharsha and Kwon, YoungOk},
  booktitle={Proceedings of the Seventeenth Workshop on Information Technology and Systems},
  pages={152--157},
  year={2007}
}

@inproceedings{bar2013improving,
  title={Improving Simple Collaborative Filtering Models Using Ensemble Methods},
  author={Bar, Ariel and Rokach, Lior and Shani, Guy and Shapira, Bracha and Schclar, Alon},
  booktitle={International Workshop on Multiple Classifier Systems},
  pages={1--12},
  year={2013},
  organization={Springer}
}

@article{begoli2019need,
  title={The Need for Uncertainty Quantification in Machine-Assisted Medical Decision Making},
  author={Begoli, Edmon and Bhattacharya, Tanmoy and Kusnezov, Dimitri},
  journal={Nature Machine Intelligence},
  volume={1},
  number={1},
  pages={20--23},
  year={2019}
}

@inproceedings{wang2019deep,
  title={Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting},
  author={Wang, Bin and Lu, Jie and Yan, Zheng and Luo, Huaishao and Li, Tianrui and Zheng, Yu and Zhang, Guangquan},
  booktitle={Proceedings of the Twenty-fifth ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={2087--2095},
  year={2019}
}

@article{rendle2012bpr,
  title={BPR: Bayesian Personalized Ranking From Implicit Feedback},
  author={Rendle, Steffen and Freudenthaler, Christoph and Gantner, Zeno and Schmidt-Thieme, Lars},
  journal={arXiv Preprint arXiv:1205.2618},
  year={2012}
}

@inproceedings{wang2018confidence,
  title={{Confidence-Aware Matrix Factorization for Recommender Systems}},
  author={Wang, Chao and Liu, Qi and Wu, Runze and Chen, Enhong and Liu, Chuanren and Huang, Xunpeng and Huang, Zhenya},
  booktitle={Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence},
  year={2018},
  pages = {434--442}
}

@inproceedings{koren2011ordrec,
  title={{OrdRec: An Ordinal Model for Predicting Personalized Item Rating Distributions}},
  author={Koren, Yehuda and Sill, Joe},
  booktitle={Proceedings of the Fifth ACM Conference on Recommender Systems},
  pages={117--124},
  year={2011}
}

@incollection{shani2011evaluating,
  title={{Evaluating Recommendation Systems}},
  author={Shani, Guy and Gunawardana, Asela},
  booktitle={Recommender Systems Handbook},
  pages={257--297},
  year={2011},
  publisher={Springer},
editor="Ricci, Francesco
and Rokach, Lior
and Shapira, Bracha
and Kantor, Paul B."
}

@inproceedings{bernardis2019estimating,
  title={{Estimating Confidence of Individual User Predictions in Item-Based Recommender Systems}},
  author={Bernardis, Cesare and Ferrari Dacrema, Maurizio and Cremonesi, Paolo},
  booktitle={Proceedings of the Twenty-seventh ACM Conference on User Modeling, Adaptation and Personalization},
  pages={149--156},
  year={2019}
}

@inproceedings{mnih2008probabilistic,
  title={{Probabilistic Matrix Factorization}},
  author={Mnih, Andriy and Salakhutdinov, Russ R.},
  booktitle={Advances in Neural Information Processing Systems},
  pages={1257--1264},
  year={2008}
}

@inproceedings{salakhutdinov2008bayesian,
  title={{Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo}},
  author={Salakhutdinov, Ruslan and Mnih, Andriy},
  booktitle={Proceedings of the Twenty-fifth International Conference on Machine Learning},
  pages={880--887},
  year={2008}
}

@article{browne2000cross,
  title={Cross-Validation Methods},
  author={Browne, Michael W},
  journal={Journal of Mathematical Psychology},
  volume={44},
  number={1},
  pages={108--132},
  year={2000}
}

@article{harper2015movielens,
  title={{The MovieLens Datasets: History and Context}},
  author={Harper, F Maxwell and Konstan, Joseph A},
  journal={ACM Transactions on Interactive Intelligent Systems},
  volume={5},
  number={4},
  pages={1--19},
  year={2015}
}

@misc{funk2006netflix,
  title={{Netflix Update: Try This at Home}},
  author={Funk, Simon},
  year={2006},
  howpublished={\url{https://sifter.org/simon/journal/20061211.html}}
}

@inproceedings{pena2020combining,
  title={{Combining Rating and Review Data by Initializing Latent Factor Models With Topic Models for Top-N Recommendation}},
  author={Pe{\~n}a, Francisco J and O'Reilly-Morgan, Diarmuid and Tragos, Elias Z and Hurley, Neil and Duriakova, Erika and Smyth, Barry and Lawlor, Aonghus},
  booktitle={Proceedings of the Fourteenth ACM Conference on Recommender Systems},
  pages={438--443},
  year={2020}
}

@article{freund1999short,
  title={A Short Introduction to Boosting},
  author={Freund, Yoav and Schapire, Robert and Abe, Naoki},
  journal={Journal-Japanese Society for Artificial Intelligence},
  volume={14},
  number={771-780},
  pages={1612},
  year={1999},
  publisher={JAPANESE SOC ARTIFICIAL INTELL}
}

@incollection{ricci2011introduction,
  title={{Introduction to the Recommender Systems Handbook}},
  author={Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
  booktitle={{Recommender Systems Handbook}},
  pages={1--35},
  year={2011},
  publisher={Springer}
}

@inproceedings{paszke2017automatic,
  title={{Automatic Differentiation in PyTorch}},
  author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  year={2017},
  booktitle={Proceedings of the NIPS 2017 Workshop on Autodiff}
}

@misc{kula2017spotlight,
  title={Spotlight},
  author={Kula, Maciej},
  year={2017},
  publisher={GitHub},
  howpublished={\url{https://github.com/maciejkula/spotlight}},
}

@inproceedings{bennett2007netflix,
  title={{The Netflix Prize}},
  author={Bennett, James and Lanning, Stan},
  booktitle={Proceedings of KDD Cup and Workshop},
  year={2007},
  pages={3--6}
}

@article{marlin2003modeling,
  title={{Modeling User Rating Profiles for Collaborative Filtering}},
  author={Marlin, Benjamin M},
  journal={Proceedings of the Sixteenth International Conference on Neural Information Processing Systems},
  pages={627--634},
  year={2003}
}

@article{ortega2021providing,
  title={{Providing Reliability in Recommender Systems Through Bernoulli Matrix Factorization}},
  author={Ortega, Fernando and Lara-Cabrera, Ra{\'u}l and Gonz{\'a}lez-Prieto, {\'A}ngel and Bobadilla, Jes{\'u}s},
  journal={Information Sciences},
  volume={553},
  pages={110--128},
  year={2021}
}

@inproceedings{barraza2017exploration,
  title={{The Exploration-Exploitation Trade-Off in Interactive Recommender Systems}},
  author={Barraza-Urbina, Andrea},
  booktitle={Proceedings of the 11th ACM Conference on Recommender Systems},
  pages={431--435},
  year={2017}
}

@inproceedings{bouneffouf2013risk,
  title={{Risk-Aware Recommender Systems}},
  author={Bouneffouf, Djallel and Bouzeghoub, Amel and Ganarski, Alda Lopes},
  booktitle={Proceedings of the International Conference on Neural Information Processing},
  pages={57--65},
  year={2013}
}

@inproceedings{gabbolini2022instability,
  title={Analyzing and Improving Stability of Matrix Factorization for Recommender Systems},
  author={D'Amico, Edoardo and Gabbolini, Giovanni and Bernardis, Cesare and Cremonesi, Paolo},
  booktitle={Journal of Intelligent Information Systems},
  year={2022}
}

@inproceedings{o2005recommender,
  title={{Recommender Systems: Attack Types and Strategies}},
  author={O'Mahony, Michael P and Hurley, Neil J and Silvestre, Gu{\'e}nol{\'e} CM},
  booktitle={Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI)},
  pages={334--339},
  year={2005}
}

@article{hullermeier2021aleatoric,
  title={Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods},
  author={H{\"u}llermeier, Eyke and Waegeman, Willem},
  journal={Machine Learning},
  volume={110},
  number={3},
  pages={457--506},
  year={2021},
  publisher={Springer}
}

@article{friedman1999rating,
  title={Rating the Rating Scales},
  author={Friedman, Hershey H and Amoo, Taiwo},
  journal={Journal of Marketing Management},
  volume={Winter},
  pages={114--123},
  year={1999}
}

@inproceedings{mcnee2003confidence,
  title={{Confidence Displays and Training in Recommender Systems}},
  author={McNee, Sean M. and Lam, Shyong K. and Guetzlaff, Catherine and Konstan, Joseph A. and Riedl, John},
  booktitle={Proceedings of IFIP INTERACT03: Human-Computer Interaction},
  volume={3},
  pages={176--183},
  year={2003}
}

@incollection{castells2015novelty,
  title={Novelty and Diversity in Recommender Systems},
  author={Castells, Pablo and Hurley, Neil J and Vargas, Saul},
  booktitle={Recommender systems handbook},
  pages={881--918},
  year={2015},
  publisher={Springer}
}

@article{winoto2010role,
  title={The Role of User Mood in Movie Recommendations},
  author={Winoto, Pinata and Tang, Tiffany Y},
  journal={Expert Systems With Applications},
  volume={37},
  number={8},
  pages={6086--6092},
  year={2010},
  publisher={Elsevier}
}

@inproceedings{dos2017gaussian,
  title={Gaussian Embeddings for Collaborative Filtering},
  author={Dos Santos, Ludovic and Piwowarski, Benjamin and Gallinari, Patrick},
  booktitle={Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={1065--1068},
  year={2017}
}

@inproceedings{cleger2013being,
  title={Being Confident About the Quality of the Predictions in Recommender Systems},
  author={Cleger-Tamayo, Sergio and Fern{\'a}ndez-Luna, Juan M and Huete, Juan F and Tintarev, Nava},
  booktitle={European Conference on Information Retrieval},
  pages={411--422},
  year={2013},
  organization={Springer}
}

@inproceedings{coscrato2022,
  title={Recommendation Uncertainty in Implicit Feedback Recommender Systems},
  author={Victor Coscrato and Derek Bridge},
  booktitle={Proceedings of the 30th Irish Conference on Artificial Intelligence and Cognitive Science},
  year={2022},
  organization={Springer}
}

@article{wang2022rethinking,
  title={Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation},
  author={Wang, Chenxu and Feng, Fuli and Zhang, Yang and Wang, Qifan and Hu, Xunhan and He, Xiangnan},
  journal={arXiv Preprint arXiv:2209.11679},
  year={2022}
}

@article{jiang2020convolutional,
  title={Convolutional Gaussian Embeddings for Personalized Recommendation With Uncertainty},
  author={Jiang, Junyang and Yang, Deqing and Xiao, Yanghua and Shen, Chenlu},
  journal={arXiv Preprint arXiv:2006.10932},
  year={2020}
}

@inproceedings{neupane2021metaedl,
  title={MetaEDL: Meta Evidential Learning for Uncertainty-Aware Cold-Start Recommendations},
  author={Neupane, Krishna Prasad and Zheng, Ervine and Yu, Qi},
  booktitle={2021 IEEE International Conference on Data Mining (ICDM)},
  pages={1258--1263},
  year={2021},
  organization={IEEE}
}

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