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Explanation-Guided Fairness Testing through Genetic Algorithm
[article]
2022
arXiv
pre-print
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The fairness characteristic is a critical attribute of trusted AI systems. A plethora of research has proposed diverse methods for individual fairness testing. ...
We first rank the explanation result 𝑒 based on the importance score assigned by the interpretable method. ...
Our experiments are conducted on a server with Ubuntu 18.04 operating system, Intel Xeon 2.50GHz CPU, NVIDIA RTX GPU, and 128GB system memory. ...
arXiv:2205.08335v1
fatcat:kwcxbsoif5ct3cq4m4i77rwee4
From chatter to headlines
2012
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12
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We propose a new methodology for recommending interesting news to users by exploiting the information in their twitter persona. ...
stream, and topic popularity in the news and in the whole twitter-land. ...
Our main recommendation system is called T.Rex, for twitter-based news recommendation system. • Our system provides personalized recommendations by leveraging information from the tweet stream of users ...
doi:10.1145/2124295.2124315
dblp:conf/wsdm/MoralesGL12
fatcat:6bkgdiirkbceziko243frivooa
Real-time top-n recommendation in social streams
2012
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12
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In this paper, we focus on analyzing social streams in real-time for personalized topic recommendation and discovery. ...
We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization -RMFX -, which uses a pairwise approach to matrix factorization in order to optimize the ...
The experiments were conducted using GNU/Linux 64-bit as operating system. None of the methods was parallelized and therefore used one single CPU for computations. ...
doi:10.1145/2365952.2365968
dblp:conf/recsys/Diaz-AvilesDSN12
fatcat:pzemwfmflndn5iqe245p6mwpru
Towards real-time collaborative filtering for big fast data
2013
Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion
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However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time. ...
, towards an individual user perspective, and ask: "What is interesting to me right now within the social media stream?". ...
CONCLUSION Our research on online collaborative filtering for social media streams provides an example of integrating large-scale recommender systems with the real-time nature of Twitter. ...
doi:10.1145/2487788.2488044
dblp:conf/www/Diaz-AvilesNDS13
fatcat:tqvscm56afhwteilaeuso5djey
Parallel methods for evidence and trust based selection and recommendation of software apps from online marketplaces
2017
Proceedings of the 12th Annual Conference on Cyber and Information Security Research - CISRC '17
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In addition to feature-based information, about these apps, these marketplaces contain large volumes of user reviews. ...
CCS CONCEPTS • Information systems ~ Trust • Information systems R ecommender systems • Social and professional topics ~ Software selection and adaptation ...
[22] experiments on a recommender system to parallelize data using Hadoop echo-system. It collects data from users, commodities, and transactions. ...
doi:10.1145/3064814.3064819
dblp:conf/csiirw/GallegeR17
fatcat:3a32egf2ijckvkw7s4dmxhi7ru
What is happening right now ... that interests me?
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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In this paper, we consider collaborative filtering as an online ranking problem and present RMFO, a method that creates, in real-time, user-specific rankings for a set of tweets based on individual preferences ...
Experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend and Weighted Regularized Matrix Factorization (WRMF ...
We introduce a novel framework for online collaborative filtering based on a pairwise ranking approach for matrix factorization, in the presence of streaming data. 2. ...
doi:10.1145/2396761.2398479
dblp:conf/cikm/Diaz-AvilesDGSN12
fatcat:bwbbyygj6jcabpypet7cp6hw7m
Performance Improvement of Stream-Centered Probabilistic Matrix Factorization Method Using Weighted Reservoir Sampling and Parallel Computing
2021
International Journal on Electrical Engineering and Informatics
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Application of the recommender system on the online platform turns out to produce a large volume of data and at an unexpected rate, making it more realistic to study the recommender system under streaming ...
Many studies have tried to develop a streaming recommender system, one of which is Stream-centered Probabilistic Matrix Factorization (SPMF). ...
Acknowledgement filtering based on a data stream management system," CEUR Workshop Proc., vol. ...
doi:10.15676/ijeei.2021.13.3.8
fatcat:kb4eyrcejrhpxgpanrarzfhuby
Guest editorial: web information technologies
2015
World wide web (Bussum)
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The second paper, by Li et al., "Social event identification and ranking on flickr", focuses on social event modeling and ranking. ...
Furthermore, event impact is defined and estimated via random walk based on the triggering relationships. ...
Yao et al. address the limitations of recommendation systems in existing techniques and cope with scenarios with only implicit feedback in the seventh paper, "A Graph-based Model for Context-aware Recommendation ...
doi:10.1007/s11280-015-0356-y
fatcat:uulmqqc4sneh3mlaqw3zlrpmly
Considering Durations and Replays to Improve Music Recommender Systems
[article]
2017
arXiv
pre-print
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A quantitative study as usually found in the literature confirms that neighborhood-based systems considering binary data give the best results in terms of MAP@k. ...
A large database was thus created using logs collected on a streaming platform, notably collecting the listening times. ...
Acknowledgements The author wishes to thank Simbals team, Deezer R&D and Recommendation teams for making this work possible, in particular Manuel Moussalam, Thomas Bouabca and Aurélien Hérault. ...
arXiv:1711.05237v1
fatcat:5t3xoh5cp5gpzaf4wqhb2mciay
Considering Durations and Replays to Improve Music Recommender Systems
2018
EAI Endorsed Transactions on Self-Adaptive Systems
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A quantitative study as usually found in the literature confirms that neighborhood-based systems considering binary data give good results in terms of MAP@k. ...
A large database was thus created using logs collected on a streaming platform, notably collecting the listening times. ...
Acknowledgements The author wishes to thank Simbals team, Deezer R&D and Recommendation teams for making this work possible, in particular Manuel Moussalam, Thomas Bouabca and Aurélien Hérault. ...
doi:10.4108/eai.5-2-2018.156379
fatcat:hbb4ojxk4fhm7ebdsnsiuay724
Tutorial on Open Source Online Learning Recommenders
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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In our tutorial, we present open source systems capable of updating their models on the fly after each event: Apache Spark, Apache Flink and Alpenglow, a C++ based Python recommender framework. ...
Recommender systems have to serve in online environments that can be non-stationary. ...
An important part of the tutorial considers the framework for evaluating recommender systems over streaming data. We rely on ideas of [1, 7] for the online DCG measure. ...
doi:10.1145/3109859.3109937
dblp:conf/recsys/PalovicsKB17
fatcat:7nigretoqncl3bxv4tc2gs2hgm
A recommender system architecture for predictive telecom network management
2015
IEEE Communications Magazine
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This work presents the design and specification of E-Stream, a predictive recommendation based solution to automated network management. ...
After observing event sequences in incoming event streams, specific appropriate actions are selected, ranked and recommended to pre-empt the predicted incidents. ...
E-STREAM COMPONENTS E-stream is a composite system to recommend corrective solutions to network scenarios based on predictive patterns leading up to network incidences. ...
doi:10.1109/mcom.2015.7010547
fatcat:36fg4bxfofbfpjz5hbdazhe42m
Short and tweet
2010
Proceedings of the 28th international conference on Human factors in computing systems - CHI '10
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We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams. ...
More and more web users keep up with newest information through information streams such as the popular microblogging website Twitter. ...
Andersen et al. discussed several key insights in their theory of trust-based recommender systems [2] , one of which is trust propagation. ...
doi:10.1145/1753326.1753503
dblp:conf/chi/ChenNNBC10
fatcat:ab3qj5jmo5azfgqhxfeebysjv4
SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
[chapter]
2014
Lecture Notes in Computer Science
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2015 pre-print arXiv:1502.08033v1 fatcat:wrt2pl2xb5dstkgadiqwmhobau
Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles. ...
In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. ...
Processing
Data Stream
2
Data Stream
Stream Processing
3 Database System Database System
4 Sensor Network Object Oriented
5 Query Processing
Data Model
(b) D2 in database domain, |K| = 2755 ...
doi:10.1007/978-3-319-11289-3_8
fatcat:wjwe7wl5rfepports423lprb2i
Recommender Systems Over Data Streams
[chapter]
2018
Encyclopedia of Big Data Technologies
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A practical recommender system displays a ranked list of a few items for which the user can give feedback. ...
In this section, we show the main differences in evaluating such systems compared to both classifiers and batch systems, as well as describe the main data stream recommender algorithms. ...
doi:10.1007/978-3-319-63962-8_328-1
fatcat:bleg7iemrrgxpbl3qlrrq2msoy
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