The Adaptive Recommendation Mechanism for Distributed Group in Mobile Environments

Cheng, Sheng-Tzong; Horng, Gwo-Jiun; Chou, Chih-Lun

doi:10.1109/tsmcc.2012.2196271

Cited by 17 publications

(3 citation statements)

References 10 publications

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“…The archive in the pseudo code stores the global-best individuals, which is the top 10% of the individuals in the population ranked according to the evaluation criteria. The individual position update formula is shown in (6).…”

Section: The Structure Of Mapso-mcmentioning

confidence: 99%

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A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

2021

KSII TIIS

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Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE + are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

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Section: The Structure Of Mapso-mcmentioning

confidence: 99%

“…Recommendation system (RS) [1] as a technology that automatically processes enormous amounts of data to mine user preference can effectively deal with the problem of information overload. Up till the present moment, RS is used in various areas of life, including entertainment [2][3][4], shopping [5], learning [4], travel [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

2021

KSII TIIS

View full text Add to dashboard Cite

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“…The main idea of RSs is to establish a model to automatically recommend the proper items to users based on the historical behavior and preference information [4]. RSs can assist users in decision making including movies, books, web search, tourism, and so on [5]. The existing RSs can be arranged into a few classes: hybrid recommendation, knowledge-based recommendation, collaborative filtering recommendation, and content-based recommendation [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Recommendation Algorithm Based on Multi-objective Evolution

Zhang

2023

ACE

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During the past two decades, recommender systems have been playing an increasingly significant role in business decisions for providing personalized recommendations. However, the majority of the present algorithms attach excess importance to accuracy while overlooking users demand for diverse items. We have proposed a novel multi-objective evolutionary recommendation algorithm, which has the capability to look for compromise among accuracy and diversity. Moreover, due to the fact that Pareto optimal solutions of recommendation tend to be sparse, the proposed algorithm implements a new strategy of population initialization with the consideration of the sparse nature of the Pareto solutions. The proposed algorithm will take effect in the recommender systems.

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An Empirical User Study on Congestion-Aware Route Recommendation

Yi,

Jin,

Bai

et al. 2024

Springer Proceedings in Business and Economics

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Overtourism has become a significant concern in many popular travel destinations around the world. As one of considerable approaches to handle the overtourism issues, congestion-aware methods can be effective in mitigating overcrowding at popular attractions by spreading tourists to less-visited areas. However, they may lead to a potential Hawk-Dove game: tourists who share the same preference may have some of them assigned worse routes than others to avoid congestion, which raises a possibility that the tourists who are assigned to relatively unfavorable routes may feel dissatisfaction and unfairness. Most existing research focuses on alleviating congestion from an overall planner perspective through simulation studies, with little emphasis on actual user experience. In this study, we conducted a user survey on congestion-aware route recommendation in Kyoto, Japan, aiming to investigate the evaluation of congestion-aware route recommendation methods from each tourist’s personal perspective and to clarify the development status and future research directions of congestion-aware route recommendation methods. We choose five congestion-aware route recommendation methods that vary in their consideration of congestion and multi-agent interactions. We reveal the strengths and weaknesses of these methods from multiple aspects. We cluster the respondents based on their text responses and explore the differences between these clusters. Furthermore, we investigate the factors affecting tourists’ experience and compare the differences among groups of tourists.

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The Adaptive Recommendation Mechanism for Distributed Group in Mobile Environments

Cited by 17 publications

References 10 publications

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

A Novel Recommendation Algorithm Based on Multi-objective Evolution

An Empirical User Study on Congestion-Aware Route Recommendation

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