A novel tourism recommender system in the context of social commerce

Esmaeili, Leila; Mardani, Shahla; Golpayegani, Seyyed Alireza Hashemi; Madar, Zeinab Zanganeh

doi:10.1016/j.eswa.2020.113301

Cited by 96 publications

(49 citation statements)

References 34 publications

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“…and his/her environment (e.g., weather and traffic conditions), or even the user's social media information (e.g., followers and followees in Twitter). For example, Esmaeili et al incorporated trust, reputation and user relationships in social communities into a collaborative filtering recommender to provide improved recommendations of tourism products [29].…”

Section: Improving Recommendations With Clustering and Association Rule Miningmentioning

confidence: 99%

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Orama¹,

Borrás²,

Moreno³

2021

Applied Sciences

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Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.

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Section: Improving Recommendations With Clustering and Association Rule Miningmentioning

confidence: 99%

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Orama¹,

Borrás²,

Moreno³

2021

Applied Sciences

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“…Currently, the use of social media is widespread and initiates new opportunities in many business sector organizations to interact with the customer, student, patients, policymakers, public, and each other [22]. The global interest of users to use social media portals has reinforced much research that focuses to discover knowledge from the publicly available [23].…”

Section: Social Mediamentioning

confidence: 99%

Interactive Recommender System for Identifying Learning Partner

Oktavia¹,

Sujarwo²

2021

IJETAE

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— Currently, higher education must open their mindset to change the learning process. This process cannot still stay on a conventional process that only involved students and lecturers as learning participants because to capture what the current industry needs and the trend of knowledge, higher education institutions must collaborate with external parties as learning partners to give a global perspective about the industrial needs and trends. The process to identify a learning partner to contribute to the learning process is not easy way. The higher education institution must select which partner has appropriate skills and competency suitable for the subject’s course. Many parameters will be involved in the selection process to identify the right partner. In this proposed system, external parties in this context consist of professionals or educators from the external institution easily can be defined to be selected as learning partners based on their competency and experience, that listed on social media LinkedIn as a professional platform. The method to build this interactive recommender system in this study is based on design science research that identifies step by step design stage to propose the best way solution for the recommender system. The result of this study is an interactive recommender system that can help higher education to find out the best candidate for their learning partner so the collaboration of learning can be implemented effectively

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“…Two or more recommendation methods are commonly combined to improve the accuracy of the recommendation results [65,66]. The combination of content-based and CF methods is one of the most popular hybrid approaches [67,68]. Other famous hybrid models are based on bioinspired and probabilistic methods, such as neural networks [40,69], genetic algorithms [70], deep learning [71], and Bayesian networks [72,73].…”

Section: Hybrid Approach To Personalized Recommendationmentioning

confidence: 99%

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Lye

Cheng

Tan

et al. 2020

Sensors

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Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

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A novel tourism recommender system in the context of social commerce

Cited by 96 publications

References 34 publications

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Interactive Recommender System for Identifying Learning Partner

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

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