A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System

Bouneffouf, Djallel; Bouzeghoub, Amel; Gançarski, Alda Lopes

doi:10.1007/978-3-642-34487-9_40

Cited by 89 publications

(69 citation statements)

References 21 publications

(25 reference statements)

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“…Existing studies propose different contextual bandit algorithms and show their empirical performances on various real-world data sets [5,6,13]. It is known that a bandit algorithm with different parameter settings can have different performance [22].…”

Section: Problem Formulationmentioning

confidence: 99%

Ensemble contextual bandits for personalized recommendation

Tang

Jiang

et al. 2014

Proceedings of the 8th ACM Conference on Recommender Systems

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The cold-start problem has attracted extensive attention among various online services that provide personalized recommendation. Many online vendors employ contextual bandit strategies to tackle the so-called exploration/exploitation dilemma rooted from the cold-start problem. However, due to high-dimensional user/item features and the underlying characteristics of bandit policies, it is often difficult for service providers to obtain and deploy an appropriate algorithm to achieve acceptable and robust economic profit.In this paper, we explore ensemble strategies of contextual bandit algorithms to obtain robust predicted click-through rate (CTR) of web objects. The ensemble is acquired by aggregating different pulling policies of bandit algorithms, rather than forcing the agreement of prediction results or learning a unified predictive model. To this end, we employ a meta-bandit paradigm that places a hyper bandit over the base bandits, to explicitly explore/exploit the relative importance of base bandits based on user feedbacks. Extensive empirical experiments on two real-world data sets (news recommendation and online advertising) demonstrate the effectiveness of our proposed approach in terms of CTR.

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Section: Problem Formulationmentioning

confidence: 99%

Ensemble contextual bandits for personalized recommendation

Tang

Jiang

et al. 2014

Proceedings of the 8th ACM Conference on Recommender Systems

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“…To recommend items to users without sufficient historical records, several studies formulate this task as a multi-armed bandit problem [14,15,5]. Multi-armed 1Õ (·) is variant of big O notation that ignores logarithmic factors.…”

Section: Related Workmentioning

confidence: 99%

Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation

Qin

Chen

Zhu

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

127

125

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Recommender systems are faced with new challenges that are beyond traditional techniques. 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, or users can hold diverse interest, but the similarity based methods may probably over-narrow it.To address the above challenges, we develop a principled approach called contextual combinatorial bandit in which a learning algorithm can dynamically identify diverse items that interest a new user. Specifically, each item is represented as a feature vector, and each user is represented as an unknown preference vector. On each of n rounds, the bandit algorithm sequentially selects a set of items according to the item-selection strategy that balances exploration and exploitation, and collects the user feedback on these selected items. A reward function is further designed to measure the quality (e.g. relevance or diversity) of the selected set based on observed feedback, and the goal of the algorithm is to maximize the total rewards of n rounds. The reward function only needs to satisfy two mild assumptions that is general enough to accommodate a large class of nonlinear functions. To solve this bandit problem, we provide algorithm that achievesÕ( √ n) regret after playing n rounds. Experiments conducted on real-wold movie recommendation dataset demonstrate that our approach can effectively address the above challenges and hence improve the performance of recommendation task.

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“…Here the decision maker's goal is to choose the arms that will maximize the expected reward given the observed contexts. Contextual bandits have been used in systems for recommending personalized content to users (8,9).…”

Section: Methods Contextual Multiarmed Bandits With Similarity Informamentioning

confidence: 99%

Data-Driven Planning for Ground Delay Programs

Estes

Ball

2017

Transportation Research Record

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This paper provides a model-based approach to planning ground delay programs. Previous research on automated planning of ground delay programs has involved the use of mathematical programming techniques. This paper proposes a data-driven method that models the problem of choosing a traffic management initiative by using the framework of the multiarmed bandit decision problem. This approach makes greater use of the available data, and suggestions made by this procedure can be shown along with data that informed the decision. This combination of tools allows decision makers to more easily evaluate suggested decisions. The paper also provides simulations of the procedure on the basis of data from Newark (New Jersey) International Airport to evaluate its effectiveness.

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A Contextual-Bandit Algorithm for Mobile Context-Aware Recommender System

Cited by 89 publications

References 21 publications

Ensemble contextual bandits for personalized recommendation

Ensemble contextual bandits for personalized recommendation

Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation

Data-Driven Planning for Ground Delay Programs

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