Attraction recommendation: Towards personalized tourism via collective intelligence

Shen, Junge; Deng, Cheng; Gao, Xinbo

doi:10.1016/j.neucom.2015.08.030

Cited by 55 publications

(46 citation statements)

References 22 publications

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“…The authors of the paper [11] solve the widespread issue of information congestion in advisory systems and the problem of incomplete data in areas with low level of tourism and non-standard routes of attractions. To solve these problems, the authors propose a new personalized travel recommendations scheme that uses explicit user interaction and multimodal travel information.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of papers [4,7,9,12] does not work with context information during attraction route visiting at all, authors of paper [11] formalizes context only as user location (city and coordinates), [14] context does not contain traffic information, [10] context contains only a topological cafeterias, [5,6] does not give many details about route planning and more focusing on ontological method for attraction processing and the authors of [8] describes only a recommendation tourism system without route planning. The proposed method in system will take into account the situation on the road, including traffic jams, current weather and season, attractiveness of sights and their schedules.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation

Mikhailov

Kashevnik

2018

MATEC Web Conf.

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Abstract. The paper presents a tourist trip planning system that is aimed at offline attractions recommendation for a tourist based on current situation in the region and user profiles. Authors present the related work analysis in this area and based on this analysis develop the reference model of the proposed system and describe implementation that is based on previously developed Tourist Assistant -TAIS.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation

Mikhailov

Kashevnik

2018

MATEC Web Conf.

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“…Another related work is Hao et al [8], in which the authors proposed to generate overviews for locations by mining representative topic tags from travelogues. Topic detection is also applied in Shen et al's study [9], where the topic features of tourist attractions were mined from user comments on travel websites and then matched with tourists' preferences to generate personalized attraction recommendation for them.…”

Section: Introductionmentioning

confidence: 99%

“…In Jiang et al's research [14], the topics about user preference were extracted from the textual description of photos on social media to model users by leveraging an expanded model of LDA, then personalized attraction recommendation was performed accordingly. In Shen et al's study [9], LDA was introduced to obtain topic and topic probability distribution of each attraction on the basis of a collection of user comments crawled from travel websites, then the similarities between attractions were measured for further attraction recommendation.…”

Section: Introductionmentioning

confidence: 99%

Topic mining of tourist attractions based on a seasonal context aware LDA model

Huang

Wang

Yang

et al. 2018

IDA

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With the rise of personalized travel recommendation in recent years, automatic analysis and summary of the tourist attraction is of great importance in decision making for both tourists and tour operators. To this end, many probabilistic topic models have been proposed for feature extraction of tourist attraction. However, existing state-of-the-art probabilistic topic models overlook the fact that tourist attractions tend to have distinct characteristics with respect to specific seasonal context. In this article, we contribute the innovative idea of using seasonal contextual information to refine the characteristics of tourist attractions. Along this line, we first propose STLDA, a season topic model based on latent Dirichlet allocation which can capture meaningful topics corresponding to various seasonal contexts for each attraction from a collection of attraction description documents. Then, an inference algorithm using Gibbs sampling is put forward to learn the posterior distributions and model parameters of our proposed model. In order to verify the effectiveness of STLDA model, we present a detailed experimental study using collected real-world textual data of tourist attractions. The experimental analysis results show that the superiority of STLDA over the basic LDA model in detecting the season-dependent topics and giving a representative and comprehensive summarization related to each tourist attraction. More importantly, it has great significance for improving the level of personalized attraction recommendation service.

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“…They continue this study in a number of ways like in community detection, the selection strategy of the start nodes i.e., the seeding strategy. Shen J, et al [12] had proposed Travel attraction recommendation system collects the knowledge from social media. Author describes the personalized attraction similarity (PAS) model to combine travel information and customer feedbacks to recommend attraction to appropriate customers.…”

Section: Introductionmentioning

confidence: 99%

Survey on Personalized Web Recommender System

Kumar¹,

Varsha²

2018

IJIEEB

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Recommendation system plays an essential role in searching any information from World Wide Web. Recommender system handles Information straining problem and improve customer correlation by providing best services. It suggests items or services to users according to their interest, navigation behavior or demographic information. This paper performs a survey on different approaches available for recommender system and performs a comparative analysis of different algorithms. Further, a discussion about various application areas has been done. At the end, issues and challenges in recommender systems have been discussed.

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Attraction recommendation: Towards personalized tourism via collective intelligence

Cited by 55 publications

References 22 publications

Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation

Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation

Topic mining of tourist attractions based on a seasonal context aware LDA model

Survey on Personalized Web Recommender System

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