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Online Clustering of Contextual Cascading Bandits
[article]
2019
arXiv
pre-print
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We consider a new setting of online clustering of contextual cascading bandits, an online learning problem where the underlying cluster structure over users is unknown and needs to be learned from a random ...
We conduct experiments on both synthetic and real data, and demonstrate the effectiveness of our algorithm and the advantage of incorporating online clustering method. ...
We first introduce previous work related to our setting, then formulates the setting of Online Clustering of Contextual Cascading Bandits with some appropriate assumptions. ...
arXiv:1711.08594v2
fatcat:7no5l22nenhvtpm2gfbpdxq6o4
Carousel Personalization in Music Streaming Apps with Contextual Bandits
[article]
2020
arXiv
pre-print
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In this paper, we model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. ...
We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app ...
Contextual Multi-Armed Bandits. Instead of relying on clusters, let us now assume that we directly have access to a D-dimensional attribute vector x u ∈ R D for each user u. ...
arXiv:2009.06546v1
fatcat:kosq7gqqgvdujo3fusww6lvngq
A Survey on Practical Applications of Multi-Armed and Contextual Bandits
[article]
2019
arXiv
pre-print
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This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. ...
The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit ...
., 2017] also study the online influence maximization problem in social networks but under the independent cascade model. ...
arXiv:1904.10040v1
fatcat:j6v37wy7f5bmvpfzzhtnutbeoa
Bandit Algorithms in Information Retrieval
2019
Foundations and Trends in Information Retrieval
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Dorota Głowacka (2019), "Bandit Algorithms in Information Retrieval", Foundations and Trends R in Information Retrieval: Vol. 13, No. 4, pp 299-424. DOI: 10.1561/1500000067. ...
The goal is to show how specific concepts related to bandit algorithms, e.g. graph clustering with bandits, or a specific family of bandit algorithms, e.g. dueling bandits developed over time. ...
This led to the development of linear and contextual bandits (Auer, 2002; Li et al., 2010b) , where a linear dependency between the expected payoff of an arm and its context is assumed. ...
doi:10.1561/1500000067
fatcat:api5ljs5abbwdckujtsgwp27o4
Improved Algorithm on Online Clustering of Bandits
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1902.09162v1 fatcat:ot7akf74evh5td5d2d5dfvk5n4
We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. ...
A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. ...
Online clustering of contextual cascading bandits. arXiv preprint arXiv:1711.08594v2, 2019. ...
arXiv:1902.09162v2
fatcat:owpw5gjzuzgkxbemk46sin55ri
On Learning to Rank Long Sequences with Contextual Bandits
[article]
2021
arXiv
pre-print
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Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses. ...
We evaluate our algorithms on a number of real-world datasets, and show significantly improved empirical performance as compared to known cascading bandit baselines. ...
Online clustering of contextual cascading bandits. In arXiv:1711.08594, 2019. S. Li and S. Zhang. Online clustering of contextual cascading bandits. ...
arXiv:2106.03546v1
fatcat:key2mb4rinf3hho4sxajfazaam
Collaborative Filtering Bandits
2016
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16
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In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. ...
bandits. ...
The work [29] combines (k-means-like) online clustering with a contextual bandit setting, but clustering is only made at the user side. ...
doi:10.1145/2911451.2911548
dblp:conf/sigir/LiKG16
fatcat:3m3aussumjco3i7qerbkxs5fxm
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)
[article]
2022
arXiv
pre-print
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We describe and compare two methods of contextual multi-armed bandits, with upper-confidence bounds on the remaining potential of influencers, one using a generalized linear model and the Good-Turing estimator ...
base of few influential nodes. ...
Acknowledgments We thank Olivier Cappé and Yoan Russac, for early discussions and ideas on modeling the distribution of rewards. ...
arXiv:2201.05231v1
fatcat:heaxmwap7fgqbm6544zwkp67ny
Collaborative Filtering Bandits
[article]
2016
arXiv
pre-print
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In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. ...
bandits. ...
The work [29] combines (k-means-like) online clustering with a contextual bandit setting, but clustering is only made at the user side. ...
arXiv:1502.03473v7
fatcat:wvbsmyhdyvbb5i7ulgqh7gpxzm
Reinforcement Learning for Online Information Seeking
[article]
2019
arXiv
pre-print
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In this paper, we give an overview of deep reinforcement learning for search, recommendation, and online advertising from methodologies to applications, review representative algorithms, and discuss some ...
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. ...
The contextual bandit model (a.k.a. associative bandits or bandits with side information) is an extension of MAB that takes additional information into account Lu et al. 2010 ]. 2.1.2 Markov Decision ...
arXiv:1812.07127v4
fatcat:pyc75g5hufcs5b3f75gonbkp24
Data-Driven Stream Mining Systems for Computer Vision
[chapter]
2014
Advances in Computer Vision and Pattern Recognition
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With advances in sensor and digital processing technologies, we are able to deploy networks involving large numbers of cameras that acquire increasing volumes of image data for diverse applications in ...
However, to exploit the potential of such extensive networks for image acquisition, important challenges must be addressed in efficient communication and analysis of such data under constraints on power ...
Acknowledgments This work is supported by the US Air Force Office of Scientific Research under the DDDAS Program. ...
doi:10.1007/978-3-319-09387-1_12
fatcat:am4bf2g7tjbd3h3vefpeaxvvum
Interactive Social Recommendation
2017
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17
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Extensive experiments on three real-world datasets illustrate the improvement of our proposed method against the state-of-the-art algorithms. ...
In addition, we also give analyses on the complexity and regret of the proposed model. ...
[24, 28] investigate adaptive clustering algorithms based on the learnt model parameters for contextual bandit under the assumption that content is recommended to di erent groups (clusters) of users ...
doi:10.1145/3132847.3132880
dblp:conf/cikm/WangHLE17
fatcat:l4xwvhl67nhs7djignsv5obrne
Optimizing Ranking Systems Online as Bandits
[article]
2021
arXiv
pre-print
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We formulate this nonstationary online learning to rank problem as cascade non-stationary bandits and propose CascadeDUCB and CascadeSWUCB algorithms to solve the problem. ...
Bandit is a general online learning framework and can be used in our optimization task. ...
Cascading non-stationary bandits: Online learning to rank in the non-stationary cascade model. In IJCAI, pages 2859-2865, August 2019 [74] . ...
arXiv:2110.05807v1
fatcat:mp3fctx6sffhjej7idwc7v33ca
Bandits Under The Influence (Extended Version)
[article]
2020
arXiv
pre-print
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We present online recommendation algorithms rooted in the linear multi-armed bandit literature. ...
A prevalent cause for the evolution of user interests is the influence of their social circle. ...
Contextual linear bandits also have a long history in online learning [1, 3, 4, 9, 10, 21] ; generalized linear bandits have also been extensively studied [1, 2, 21] . ...
arXiv:2009.10135v1
fatcat:s5df3dif3neqlfrbl43lirau3m
May AI?
2019
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19
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The paper presents cooperative contextual bandits (CCB) as a machine-learning method for interactive ideation support. ...
In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. ...
Cooperative Contextual Bandits We adapted an online learning cooperative contextual bandit algorithm presented by Tekin et al. ...
doi:10.1145/3290605.3300863
dblp:conf/chi/KochLHO19
fatcat:4lswl2ykpvdrzeql7sk3jjfpk4
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