A context-aware personalized travel recommendation system based on geotagged social media data mining

Majid, Abdul; Chen, Ling; Chen, Gencai; Mirza, Hamid Turab; Hussain, Ibrar; Woodward, John R.

doi:10.1080/13658816.2012.696649

Cited by 190 publications

(134 citation statements)

References 25 publications

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“…From a methodological perspective, travel patterns and routes taken by tourists between main attractions are conventionally modeled by the Markov chain-based approach [18,19] or collaborative filtering [20,21]. It generally requires the detection of frequent travel sequences to gain a deep insight into the tourists' travel behaviors.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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Abstract:With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

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Section: Introductionmentioning

confidence: 99%

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Liu

et al. 2017

IJGI

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“…We set 20% users as a test set to determine their interest preferences, calculate the similarity between users, and use RecUFG recommendation algorithm recommend candidate sites for users. We set a comparison experiment, compare with the traditional collaborative filtering methods UserCF, Personalized Context-aware Rank (PCR) method [25]. The accuracy of different recommendation algorithm is shown in Fig.…”

Section: Effect Of Location On User Check-in Behaviormentioning

confidence: 99%

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Zhang

Pan

et al. 2016

Cybernetics and Information Technologies

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“…They aimed to recommend to the user minimum distances with maximum tourism popularity. Majid et al [37] presented an approach for personalization and recommendation of tourist locations. They obtained users' preferences from their travel history in a city and used this information to recommend locations in an another, unknown city.…”

Section: ) Recommendation Systemsmentioning

confidence: 99%

An Overview of Flickr Challenges and Research Opportunities

Spyrou

Mylonas

2014

2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization

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Abstract-It is rather true that nowadays diversity and a potential for growth characterize the field of popular social networks like Flickr. Based on this observation, we provide herein a brief overview of the basic characteristics, challenges and research opportunities of the latter. Some historical facts of its origin are presented, followed by a review of its digital photo sharing and tagging capabilities, focusing on the most popular related research fields, like travel applications, knowledge extraction and human activity tracking. Throughout the paper the status of current research and directions for future research are highlighted in an effort to provide useful information to fellow researchers.

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A context-aware personalized travel recommendation system based on geotagged social media data mining

Cited by 190 publications

References 25 publications

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

An Overview of Flickr Challenges and Research Opportunities

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