An extensive study on the evolution of context-aware personalized travel recommender systems

Renjith, Shini; Sreekumar, A.; Jathavedan, M.

doi:10.1016/j.ipm.2019.102078

Cited by 114 publications

(54 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In subsectors with high substitutability, such as flight and hotel bookings, global companies adopted assortative matching algorithms to underpin their marketing engines [96][97][98][99]. Research has attempted to adapt and codify travel agent practices to generate specialist algorithms [100][101][102][103], and to predict whether or not these so-called online travel agents, OTAs, will supersede human expertise [104,105]. In practice, however, we suggest that these first-generation specialist systems are already being superseded.…”

Section: Specialist Travel and Tourism Algorithmsmentioning

confidence: 99%

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Buckley

Cooper²

2021

Sustainability

View full text Add to dashboard Cite

We propose that assortative matching, a well-established paradigm in other industry sectors and academic disciplines, can underpin the concept of destination matching. This provides a new foundation to integrate research concepts and terminology in destination marketing and destination choice. We argue that the commercial tourism industry already applies destination matching approaches, with three historical phases. Initially, matching of tourists and destinations relied on the tacit expertise of specialist agents. This still applies in specialist subsectors. For generalist travel and accommodation, human agents were partially replaced by online travel agents, OTAs, which are customised algorithms operating only in the travel sector. These still exist, but their share price trends suggest decreasing significance. Currently, automated assortative algorithms use multiple sources of digital data to push appealing offers to potential purchasers, across all retail sectors. Digital marketing strategies for tourism products, enterprises, and destinations are now just one category of generalised product–purchaser matching, using entirely automated algorithms. Researchers do not have access to proprietary algorithms, but we can identify which components they incorporate by analysing their underlying patents. We propose that theories of destination marketing and choice need to reflect these recent and rapid real-world changes via deliberate analysis of destination matching.

show abstract

Section: Specialist Travel and Tourism Algorithmsmentioning

confidence: 99%

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Buckley

Cooper²

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Randomly select the negative sample l ne . Suppose the number of negative samples is k ne , then L V can be formulated as: (12) For the whole training dataset, the users' visual preference for the tourist attractions can be modeled as Equation 13: (13) 3. 4…”

Section: Visual Embeddingmentioning

confidence: 99%

“…It can be concluded that both of the recommendation methods have their disadvantages, leading to problems of insufficient recommending accuracy in some scenarios. Therefore, the hybrid recommendation methods that fuse both methods' advantages have gradually become a trend [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

Han

Liu

Chen

et al. 2021

IJGI

View full text Add to dashboard Cite

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.

show abstract

“…The places of the user's interest are found and ranked using opinion mining and recommended to the user. Here comes the role of the tourism recommender system which was introduced by Delgado and Davidson [5]. The prominent filtering mechanisms used in RS are Collaborative filtering, Contentbased filtering, context-aware filtering and Hybrid filtering.…”

Section: Approaches Of Recommender Systemmentioning

confidence: 99%

The evolution of travel recommender systems: A comprehensive review

Muneer¹,

Basheer²

2020

Malaya J. Mat

View full text Add to dashboard Cite

Travel has always been innate desire of human beings. People wanted to travel irrespective of any hurdles like geographical barrier, age, gender, or colour with different motivations. Nowadays travel and adventure became the most trending entertainment as well. Planning a trip is a time-consuming and herculean task for inexperienced travelers. Here comes the possibility of expert opinion for scheduling a perfect travel plan. With the development of information technology and social media, there are numerous possibilities and opportunities in fetching suitable information which can turn out to set up an appropriate travel plan and hence enhance the quality of travel. The significance of a Recommender System (RS) comes in the picture which can address travel-related queries. Personalized Travel RS will add more customization and user-specific features than Automatic Travel RS. In this paper, we conducted a detailed review and chronological evolutions of various methods and techniques used in the travel and tourism sector and compared their efficiency in Recommendations.

show abstract

An extensive study on the evolution of context-aware personalized travel recommender systems

Cited by 114 publications

References 44 publications

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

Assortative Matching of Tourists and Destinations: Agents or Algorithms?

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

The evolution of travel recommender systems: A comprehensive review

Contact Info

Product

Resources

About