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Alternating Linear Bandits for Online Matrix-Factorization Recommendation
[article]
2018
arXiv
pre-print
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In this paper, we propose a novel algorithm for online matrix factorization recommendation that combines linear bandits and alternating least squares. ...
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. ...
Alternating Linear Bandits
for Online Matrix-Factorization Recommendation ...
arXiv:1810.09401v1
fatcat:h4mm75e4sngrjikkgxg33y4f5a
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. ...
Recommender systems should adapt to user interests as the latter evolve. A prevalent cause for the evolution of user interests is the influence of their social circle. ...
LinUCB Algorithm LinUCB [21] is an alternative to LinREL for linear bandits. Unfortunately, in our case, it leads to an intractable problem. ...
arXiv:2009.10135v1
fatcat:s5df3dif3neqlfrbl43lirau3m
Fast Distributed Bandits for Online Recommendation Systems
[article]
2020
arXiv
pre-print
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Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. ...
Recent recommendation algorithms that learn clustering or social structures between users have exhibited higher recommendation accuracy. ...
RL-based online recommendation algorithms: Reinforcement learning (RL) techniques are an alternative to bandit-based recommendation systems. ...
arXiv:2007.08061v1
fatcat:6zlznh2cjbe5xo76ulcycgh5y4
Bandits Warm-up Cold Recommender Systems
[article]
2014
arXiv
pre-print
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Overall, the goal of this paper is to bridge the gap between recommender systems based on matrix factorizations and those based on contextual bandits. ...
Then, we propose an online setting closer to the actual use of recommender systems; this setting is inspired by the bandit framework. ...
We also introduce a methodology to use a classical partially filled rating matrix to assess the online performance of a bandit-based recommendation algorithm. ...
arXiv:1407.2806v1
fatcat:fkomqud3avgzjeqkp3p6ywydae
Bandits and Recommender Systems
[chapter]
2015
Lecture Notes in Computer Science
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Overall, the goal of this paper is to bridge the gap between recommender systems based on matrix factorizations and those based on contextual bandits. ...
Then, we propose an online setting closer to the actual use of recommender systems; this setting is inspired by the bandit framework. ...
We also introduce a methodology to use a classical partially filled rating matrix to assess the online performance of a bandit-based recommendation algorithm. ...
doi:10.1007/978-3-319-27926-8_29
fatcat:627el3ubinblvkjbd4rcczcdpq
Deep neural network marketplace recommenders in online experiments
[article]
2018
arXiv
pre-print
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This paper focuses on the challenge of measuring recommender performance and summarizes the online experiment results with several promising types of deep neural network recommenders - hybrid item representation ...
models combining features from user engagement and content, sequence-based models, and multi-armed bandit models that optimize user engagement by re-ranking proposals from multiple submodels. ...
Similar to (i), we factorize a user-postcode matrix
D t ≥tt h by a specific date tt h . ...
arXiv:1809.02130v1
fatcat:hbe62jvfhvcp5i3bfvmaoulgoa
Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation
[chapter]
2014
Proceedings of the 2014 SIAM International Conference on Data Mining
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For example, most traditional techniques are based on similarity or overlap among existing data, however, there may not exist sufficient historical records for some new users to predict their preference ...
Recommender systems are faced with new challenges that are beyond traditional techniques. ...
ratings for 3900 movies by 6040 users of online movie recommendation service [19] . ...
doi:10.1137/1.9781611973440.53
dblp:conf/sdm/QinCZ14
fatcat:v36nne7k4vbyxihof7mqprdipi
A Linear Bandit for Seasonal Environments
[article]
2020
arXiv
pre-print
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Contextual bandit algorithms are extremely popular and widely used in recommendation systems to provide online personalised recommendations. ...
In the music recommendation scenario for instance, people's music taste can abruptly change during certain events, such as Halloween or Christmas, and revert to the previous music taste soon after. ...
INTRODUCTION Bandit algorithms are extremely popular and widely used in recommender systems, due to their ability to efficiently deal with the exploration-exploitation trade-off in an online fashion. ...
arXiv:2004.13576v1
fatcat:k4cf436vfbaijgo2ofza2glyrq
Contextual Bandits for adapting to changing User preferences over time
[article]
2020
arXiv
pre-print
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Next we develop a novel algorithm for solving the contextual bandit problem. ...
Similar to the linear bandits, this algorithm maps the reward as a function of context vector but uses an array of learners to capture variation between actions/arms. ...
We will compare the 2 and create a matrix of accuracy. ...
arXiv:2009.10073v2
fatcat:dxg4vd6xvbeklmtpgqumynex5e
Bayesian Linear Bandits for Large-Scale Recommender Systems
[article]
2022
arXiv
pre-print
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We develop a decision-making policy for a linear bandit problem with high-dimensional context vectors and several arms. ...
That is especially challenging when the decision-maker has a variety of items to recommend. In this paper, we build upon the linear contextual multi-armed bandit framework to address this problem. ...
Index Terms-Recommender systems, decision-making, online learning, multi-armed bandit
I. ...
arXiv:2202.03167v1
fatcat:owzxaod45bbh3fvutxlghmkgse
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
[article]
2014
arXiv
pre-print
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To provide up-to-date results for these complex models, Velox also facilitates lightweight online model maintenance and selection (i.e., dynamic weighting). ...
Velox is a data management system for facilitating the next steps in real-world, large-scale analytics pipelines: online model management, maintenance, and serving. ...
Hellerstein, Tomer Kaftan, Henry Milner, Ion Stoica, Vikram Sreekanti, and the anonymous CIDR reviewers for their thoughtful feedback on this work. ...
arXiv:1409.3809v2
fatcat:33a5muyjlbhmrjxn2zkwxlxpxq
Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding
[article]
2021
arXiv
pre-print
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Previous work showed that the problem can be represented as a linear bandit with visual context. ...
In general, we propose a modified linear contextual bandit framework augmented with post hoc context observed after action selection to empirically increase learning speed and reduce cumulative regret. ...
If the post hoc context model is known perfectly, it can recommend the correct action for a given context after only a single attempt, cutting down exploration by a factor of K. ...
arXiv:2011.02604v2
fatcat:35kyenmzwvc5vmznmfxocbmeu4
Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
[article]
2022
arXiv
pre-print
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2020 pre-print arXiv:2007.15788v1 fatcat:6ypiw4tdhzgtxni5msrlcelrji
Multi-dimensional online decision making plays a crucial role in many real applications such as online recommendation and digital marketing. ...
We propose two learning algorithms tensor elimination and tensor epoch-greedy for tensor bandits without context, and derive finite-time regret bounds for them. ...
., 2011] for linear bandits with finitely many arms. ...
arXiv:2007.15788v2
fatcat:ie2sagqvv5ay7i7ugkqdtz2idy
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
[article]
2018
arXiv
pre-print
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We believe that this is an important step forward for the field of recommendation systems research, that could open up an avenue of collaboration between the recommender systems and reinforcement learning ...
To this end we introduce RecoGym, an RL environment for recommendation, which is defined by a model of user traffic patterns on e-commerce and the users response to recommendations on the publisher websites ...
The recommendation literature on modeling organic user behavior is vast: often, organic recommendation is framed as a user-item matrix completion task with matrix factorization as a common approach [6 ...
arXiv:1808.00720v2
fatcat:img6bmapjjak3a27wuicmta5pi
Interactive collaborative filtering
2013
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13
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We formulate the interactive CF with the probabilistic matrix factorization (PMF) framework, and leverage several exploitation-exploration algorithms to select items, including the empirical Thompson sampling ...
Bringing the interactive mechanism back to the CF process is fundamental because the ultimate goal for a recommender system is about the discovery of interesting items for individual users and yet users ...
For instance, latent factor models have become quite popular during the recent years [20] , while the matrix factorization techniques [22] have shown their effectiveness in various settings such as ...
doi:10.1145/2505515.2505690
dblp:conf/cikm/ZhaoZW13
fatcat:tzufqxtwtzffxjcimiqnqnbnfy
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