Tourism recommendation system based on semantic clustering and sentiment analysis

Abbasi-Moud, Zahra; Vahdat-Nejad, Hamed; Sadri, Javad

doi:10.1016/j.eswa.2020.114324

Cited by 143 publications

(50 citation statements)

References 33 publications

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“…It has become the residents' bad habit. Convenience and some attractions affect several beaches, with moderate category values in management (Abbasi-Moud et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

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Evaluation and Application of New-Semarang Coastal Resources Management

Prayitno¹,

Purwanti²,

Indarjo³

2021

JST

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The beach in Semarang’s city has a variety of uniqueness with a variety of types and potential. The beach located on the north coast of Semarang city has various characteristics and strengths, and weaknesses. In overcoming differences in management and avoiding misunderstandings between governments, it prioritizes managing the coastal location. At least four integrated coastal management sub-systems consist of Human Resources Management, Accessibility, Beach Location Infrastructure, and Management Policies. This research was conducted from April 2020 until August 2020, to determine the most appropriate alternative strategy to be chosen and implemented in coastal location management. Study analysis with an Analytical Hierarchy Process-AHP analytical technique use with Application R - Open sources. The integrated coastal area management can as the most appropriate coastal location management strategy to implement the current situation and future planning. Alternative methods planned for use are the Availability of a Security Guard, Ease of Access with Signage, Parking Lots, and Rule or regulations to an Integrated Location. The study results stated that the best strategy is management collaboration in the form of interrelated rules.

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“…It has become the residents' bad habit. Convenience and some attractions affect several beaches, with moderate category values in management (Abbasi-Moud et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…Existing policies from the past few years need to be evaluated and improved according to the existing access. Analysis of tourist visits is required to manage without building new locations (Abbasi-Moud et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Evaluation and Application of New-Semarang Coastal Resources Management

Prayitno¹,

Purwanti²,

Indarjo³

2021

JST

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“…At present, quite a few researchers have proposed related methods. In [22], Abbasi-Moud et al used semantic clustering and sentiment analysis to extract user preference information from user review text. In [23], Zhao et al calculated the sentiment deviation of the user's review text and integrated it into the matrix decomposition to improve the accuracy of the score prediction.…”

Section: Sentiment Analysis and Its Application Inmentioning

confidence: 99%

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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To comply with the rapid development of big data in mobile services, an increasing number of websites have begun to provide users with recommendation decisions in various areas, like shopping, tourism, food, and medical treatment. However, there are still some challenges in the field of medical recommendation systems, such as the lack of personalized medical recommendations and the problem of data sparseness, which seriously restricts the effectiveness of such recommendations. In this paper, we propose a personalized medical recommendation method based on a convolutional neural network that integrates revised ratings and review text, called revised rating and review based on a convolutional neural network (RR&R-CNN). First, the review text is divided into user and doctor datasets, and BERT vectorized representations are performed on them. Moreover, the original rating features are revised by adding the sentiment analysis values of the review text. Then, the vectorized review text and the revised rating features are spliced together and input into the convolutional neural network to extract the deep nonlinear feature vectors of both users and doctors. Finally, we use a factorization machine for feature interaction. We conduct comparison experiments based on a Yelp dataset in the “Health & Medical” category. The experimental results confirm the conclusion that RR&R-CNN has a better effect compared to a traditional method.

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“…Furthermore, they examined different dimensions of persuasion knowledge by exploring to what extent the objective and subjective persuasion knowledge have differential impacts on consumers' benefit. Abbasi-Moud et al [14] introduced a tourism recommendation system that extracts users' preferences in order to provide personalized recommendations. To this end, users' reviews on tourism in social networks are used as a rich source of information to extract preferences.…”

Section: Related Workmentioning

confidence: 99%

An Edge-based Strategy for Smart Advertising

Seyghaly

García

Masip-Bruin

2021

2021 International Conference on Computer Communications and Networks (ICCCN)

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Smart advertising creates awareness about some offer with a more direct, personalized and interactive focus. In this area, AROUND is a social network aimed at providing smart advertising to suggest appealing business to their customers and friends. The AROUND system is supported by a sophisticated recommender system, which considers not only the customers historical behaviours, but also their current mood and accurate location. In such smart recommendation systems, the response time for the personalized advertising is critical for a successful users' quality of experience. In this research work we first evaluate the current performance of the AROUND system in terms of processing and communication times considering that, nowadays, this social network has more than 3 million users. The current implementation of the system relies on the deployment of a network of beacons, and uses a domestic cloud provider as the main infrastructure. We show that when the number of concurrent requests becomes too high, the response time faces some limitations. In order to address this issue, we discuss several alternatives, and propose the use of an edge-based strategy as a solution for fast response time. In the experimental section, we measure the performance of the AROUND system, both in our current infrastructure at the cloud and with an edge-based approach, and show the additional advantages of leveraging the edge-based strategy even in the case of overloading the cloud capacity.

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Tourism recommendation system based on semantic clustering and sentiment analysis

Cited by 143 publications

References 33 publications

Evaluation and Application of New-Semarang Coastal Resources Management

Evaluation and Application of New-Semarang Coastal Resources Management

Deep Personalized Medical Recommendations Based on the Integration of Rating Features and Review Sentiment Analysis

An Edge-based Strategy for Smart Advertising

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