Hybrid-ε-greedy for Mobile Context-Aware Recommender System

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

doi:10.1007/978-3-642-30217-6_39

Cited by 17 publications

(11 citation statements)

References 7 publications

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“…TITLE-ABS-KEY( ("reinforcement learning" OR "contextual bandit") AND ("personalization" OR "personalized" OR "personal" OR "personalisation" OR "personalised" OR "customization" OR "customized" OR "customised" OR "customised" OR "individualized" OR "individualised" OR "tailored")) Table 7 Table containing all included publications. The first column refers to the data items in Table 2 # Value Publications 1 n [1,4,10,11,13,16,[18][19][20][24][25][26][27][28][29]31,32,35,36,38,[40][41][42][43][44][45]48,49,52,56,63,66,68,70,74,82,85,86,88,91,93,94,99,101,104,[106]…”

Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.

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Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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“…Over the years, there has been a steady increase of research activities in mobile environment. Although, recommendation in mobile environment have largely been researched and recommendations are proposed by the use of contexts information (see [70][71][72][73][74][75]), mobile devices and their associated apps require contextual user information to suggest the correct apps based on their location and contexts. Cold start recommendation in mobile environment can arguably be associated with various and diverse set of possibilities for exploiting and obtaining auxiliary information.…”

Section: Bmentioning

confidence: 99%

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

et al. 2021

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Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.

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“…On the contrary, the agent will randomly choose the next action with the probability of . This helps the agent escape from the local optimum dilemma and explores unknown sequences [23]. is set to a large initial value to encourage exploration in the early stages and linearly decreases for the effectiveness of convergence.…”

Section: A Reinforcement Learningmentioning

confidence: 99%

Sequential Node Attack of Complex Networks Based on Q-Learning Method

Fang

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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The security issue of complex network systems, such as communication systems and power grids, has attracted increasing attention due to cascading failure threats. Many existing studies have investigated the robustness of complex networks against cascading failure from an attacker's perspective. However, most of them focus on the synchronous attack in which the network components under attack are removed synchronously rather than in a sequential fashion. Most recent pioneering work on sequential attack designs the attack strategies based on simple heuristics like degree and load information, which may ignore the inside functions of nodes. In the paper, we exploit a reinforcement learning-based sequential attack method to investigate the impact of different nodes on cascading failure. Besides, a candidate pool strategy is proposed to improve the performance of the reinforcement learning method. Simulation results on Barabási−Albert scale-free networks and real-world networks have demonstrated the superiority and effectiveness of the proposed method.

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Hybrid-ε-greedy for Mobile Context-Aware Recommender System

Cited by 17 publications

References 7 publications

Reinforcement learning for personalization: A systematic literature review

Reinforcement learning for personalization: A systematic literature review

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Sequential Node Attack of Complex Networks Based on Q-Learning Method

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