Please enable JavaScript.
Coggle requires JavaScript to display documents.
Rurax - Coggle Diagram
Rurax
Produtos/Serviços
Datasets
https://www.kaggle.com/olistbr/brazilian-ecommerce
https://www.kaggle.com/marlesson/meli-data-challenge-2020
https://github.com/MengtingWan/marketBias
Papers
A trust-based collaborative filtering algorithm for E-commerce recommendation system
-
https://link.springer.com/article/10.1007/s12652-018-0928-7
XIAO, Ying; EZEIFE, C. I.
E-Commerce product recommendation using historical purchases and clickstream data
. In: International Conference on Big Data Analytics and Knowledge Discovery. Springer, Cham, 2018. p. 70-82.-
https://cezeife.myweb.cs.uwindsor.ca/dawak18_pp72to82.pdf
outros
https://implicit.readthedocs.io/en/latest/
Jobs
Datasets
https://www.kaggle.com/c/job-recommendation
https://github.com/tej-prash/Job-Recommendation-System
Papers
https://coggle.it/diagram/XuMC4a6wtdq_v9k3/t/location-data-for-recommender-systems
PALOMARES, Iván et al.
Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation
. Information Fusion, v. 69, p. 103-127, 2021.
JACOBSEN, Anik; SPANAKIS, Gerasimos.
It's a Match! Reciprocal Recommender System for Graduating Students and Jobs
. In: EDM. 2019. -
https://www.researchgate.net/profile/Gerasimos-Spanakis/publication/334230611_It%27s_a_Match_Reciprocal_Recommender_System_for_Graduating_Students_and_Jobs/links/5d1dfd71a6fdcc2462bf9cbc/Its-a-Match-Reciprocal-Recommender-System-for-Graduating-Students-and-Jobs.pdf
ÖZCAN, Gözde; ÖĞÜDÜCÜ, Şule Gündüz.
Applying classifications techniques in job recommendation system for matching of candidates and advertisements
. Int. J. Intell. Comput. Res, v. 8, n. 1, p. 9, 2017. -
https://infonomics-society.org/wp-content/uploads/ijicr/published-papers/volume-8-2017/Applying-Classifications-Techniques-in-Job-Recommendation-System-for-Matching-of-Candidates-and-Advertisements.pdf
JIANG, Miao et al.
User click prediction for personalized job recommendation
. World Wide Web, v. 22, n. 1, p. 325-345, 2019.
REUSENS, Michael et al.
A note on explicit versus implicit information for job recommendation
. Decision Support Systems, v. 98, p. 26-35, 2017.
YANG, Shuo et al.
Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach
. Knowledge-Based Systems, v. 136, p. 37-45, 2017. -
https://starling.utdallas.edu/assets/pdfs/HRSPreprint.pdf
Bandit
Fairness
https://coggle.it/diagram/X005msZTxnD8w9Km/t/fairness-em-recsys
Metevier, B., Giguere, S., Brockman, S., Kobren, A., Brun, Y., Brunskill, E., & Thomas, P. S. (n.d.).
Offline Contextual Bandits with High Probability Fairness Guarantees.
(2019).
https://github.com/sgiguere/RobinHood-NeurIPS-2019
Chen, Y., Cuellar, A., Luo, H., Modi, J., Nemlekar, H., & Nikolaidis, S. (n.d.).
Fair Contextual Multi-Armed Bandits: Theory and Experiments
. (2019)
Chen, Y., Cuellar, A., Luo, H., Modi, J., Nemlekar, H., & Nikolaidis, S. (2020).
The Fair Contextual Multi-Armed Bandit.
Retrieved from www.ifaamas.org
Singh, A., & Joachims, T. (2018).
Fairness of Exposure in Rankings
, 10.
https://doi.org/10.1145/3219819.3220088
Yang, K., & Stoyanovich, J. (2017).
Measuring Fairness in Ranked Outputs
, (6). 2017
https://doi.org/10.1145/3085504.3085526
Liu, W., Liu, F., Tang, R., Liao, B., Chen, G., & Heng, P. A. (2020).
Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12084 LNAI, pp. 155–167). Springer.
https://doi.org/10.1007/978-3-030-47426-3_13
Advances in Knowledge Discovery and Data Mining
https://pakdd2020.org/download/conference_paper_slides/main-259.pdf
Datasets: MoviesLens, Kiva.org
Patro, G. K., Biswas, A., Ganguly, N., Gummadi, K. P., & Chakraborty, A. (2020).
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms.
In The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (pp. 1194–1204). New York, NY, USA: Association for Computing Machinery, Inc.
https://doi.org/10.1145/3366423.3380196
Dataset
: Google Local Ratings Dataset, Last.fm Dataset
Facets of Fairness in Search and Recommendation
(2020) -
https://arxiv.org/pdf/2008.01194.pdf
A Framework for Fairness in Two-Sided
Marketplaces
(2020) -
https://arxiv.org/pdf/2006.12756.pdf
Mehrotra, R., Mcinerney, J., Bouchard, H., Lalmas, M., & Diaz, F. (n.d.).
Towards a Fair Marketplace : Counterfactual Evaluation of the trade-off between Relevance , Fairness & Satisfaction in Recommendation Systems.
2018
Joseph, M., Kearns, M., Morgenstern, J., & Roth, A. (2016).
Fairness in Learning: Classic and Contextual Bandits
*.
Wang L., Bai Y., Sun W., Joachims T. (2021).
Fairness of Exposure in Stochastic Bandits
Patil, V., Ghalme, G., Nair, V., & Narahari, Y. (2020).
Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
.
Gangan, Elena; Kudus, Milos; Ilyushin, Eugene (2021).
Survey of multiarmed bandit algorithms applied to recommendation systems.
Imóveis/Arrendamentos