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Online Clustering of Contextual Cascading Bandits [article]

Shuai Li
2019 arXiv   pre-print
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]

Walid Bendada and Guillaume Salha and Théo Bontempelli
2020 arXiv   pre-print
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]

Djallel Bouneffouf, Irina Rish
2019 arXiv   pre-print
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

Dorota Glowacka
2019 Foundations and Trends in Information Retrieval  
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]

Shuai Li, Wei Chen, Shuai Li, Kwong-Sak Leung
2019 arXiv   pre-print
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]

Anirban Santara, Claudio Gentile, Gaurav Aggarwal, Shuai Li
2021 arXiv   pre-print
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

Shuai Li, Alexandros Karatzoglou, Claudio Gentile
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
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]

Alexandra Iacob, Bogdan Cautis, Silviu Maniu
2022 arXiv   pre-print
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]

Shuai Li and Alexandros Karatzoglou and Claudio Gentile
2016 arXiv   pre-print
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]

Xiangyu Zhao and Long Xia and Jiliang Tang and Dawei Yin
2019 arXiv   pre-print
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]

Shuvra S. Bhattacharyya, Mihaela van der Schaar, Onur Atan, Cem Tekin, Kishan Sudusinghe
2014 Advances in Computer Vision and Pattern Recognition  
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

Xin Wang, Steven C.H. Hoi, Chenghao Liu, Martin Ester
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
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]

Chang Li
2021 arXiv   pre-print
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]

Silviu Maniu, Stratis Ioannidis, Bogdan Cautis
2020 arXiv   pre-print
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?

Janin Koch, Andrés Lucero, Lena Hegemann, Antti Oulasvirta
2019 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19  
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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