A Composite Recommendation System for Planning Tourist Visits

Benouaret, Idir; Lenné, Dominique

doi:10.1109/wi.2016.0110

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…negative sentiment could be related to mess from falling leaves, which could be mitigated through additional maintenance), or whether park use can be promoted at optimal times. In addition to urban planning, we can also improve existing tour recommendation and route planning systems [6,17,22,47] by using our sentiment analysis approach to identify and recommend Points-of-Interest that elicit more positive sentiments.…”

Section: Discussion Of Main Findingsmentioning

confidence: 99%

The Grass is Greener on the Other Side

Lim

Lee

Kendal

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research.

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Section: Discussion Of Main Findingsmentioning

confidence: 99%

The Grass is Greener on the Other Side

Lim

Lee

Kendal

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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“…Clusters 1 show three dots representing the value of each data, this is because the data have similarities between one data with another so that the variation of the data there are only three differences. The data in cluster 1 has data groups (7,1) (8,1) and (8,3) and on cluster 2 show two dots consisting of data (7,9) and (8,9). While in cluster 3 show five points with data (7,2), (9,2), (9,1) (10,2) and (10,1).…”

Section: K-meansmentioning

confidence: 95%

“…The personalized search aims to help users identify desired products or services based on their personal preferences [1] specifically for travelers providing user history information [8]. In the recommended application, there is a need to recommend a user-selectable package [9]. Sophisticated recommendations result in higher customer satisfaction [10].…”

Section: Introductionmentioning

confidence: 99%

GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

Awangga

Pane

Wijayanti

2019

emitter

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A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts.

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“…The trip planning of itineraries to tourist places has incorporated the user's relevance, location, and travel time between POI [110][111][112]. Some travel recommendation methods [113][114][115] generated a list of POIs that matched the user's preferences obtained from geotagged photographs and comments from tourist experiences posted on social media.…”

Section: Tourist Contextmentioning

confidence: 99%

“…The fifth blue cluster is oriented to implementing recommenders and context-sensitive mobile applications supported in the ubiquitous computing infrastructure [89,93,[126][127][128][130][131][132][133]. It is worth highlighting the importance of the user's context in the planning of tourist trips [44,59,104,105,[110][111][112].…”

mentioning

confidence: 99%

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

2020

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Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders.

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A Composite Recommendation System for Planning Tourist Visits

Cited by 11 publications

References 10 publications

The Grass is Greener on the Other Side

The Grass is Greener on the Other Side

GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

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