An Investigation on Personalized Point-of-Interest Recommender System for Location-Based Social Networks in Smart Cities

Ibrahim, N. Asik; Rajalakshmi, E.; Vijayakumar, V.; Elakkiya, R.; Subramaniyaswamy, V.

doi:10.1007/978-3-030-72139-8_13

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…In recent years, due to the characteristics of recommendation systems that facilitate users to filter information, the application scope of recommendation systems has been expanding, from product recommendation, to news recommendation, music recommendation, etc. [6]. As shown in Figure 2, the analysis data of the tourism industry in 2015 shows that the number of tourists is increasing every year, which promotes this date to become a new application hotspot of the recommendation system.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Wang

Qin

Tang

et al. 2022

Journal of Function Spaces

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With the deepening of tourists’ demand for tourism services, the personalization of online tourists has gradually become an application of personalized recommendation technology. According to the application requirements of personalized scenic spot recommendation, this paper uses social networks and Bayesian networks to fully mine the matching degree between users and scenic spots for personalized recommendation. Add social network factors to the recommendation of tourist attractions, and fully tap the social network relationship between users. First, the users are clustered by the coupling bidirectional clustering algorithm. Then, DBSCAN (density-based noise application spatial clustering) algorithm is used to cluster scenic spots. Finally, two stable user sets and scenic spot sets are applied to the personalized recommendation algorithm to predict the user’s next upcoming scenic spot. The algorithm is compared with some traditional algorithms in the dataset. The algorithm deals with the similarity of user attributes and user behavior and uses content-based algorithm to deal with the relationship between scenic spots; com datasets have better performance.

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Section: Introductionmentioning

confidence: 99%

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Wang

Qin

Tang

et al. 2022

Journal of Function Spaces

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A Privacy-Aware K-Nearest Neighbor Query Scheme for Location-Based Services

Qi,

Jia,

Luo

et al. 2024

IEEE Internet Things J.

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A Systematic Study on Reinforcement Learning Based Applications

et al. 2023

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We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications.

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An Investigation on Personalized Point-of-Interest Recommender System for Location-Based Social Networks in Smart Cities

Cited by 3 publications

References 18 publications

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

A Privacy-Aware K-Nearest Neighbor Query Scheme for Location-Based Services

A Systematic Study on Reinforcement Learning Based Applications

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