A user-location vector based approach for personalised tourism and travel recommendation

Ajantha, D.; Vijay, J.; Ramaswamy, Sridhar

doi:10.1109/icbdaci.2017.8070880

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…To address these issues H. Wang et. Al [39], introduced Collaborative Topic Regression (CTR), which is a tightly coupled method and a probabilistic graphical model. Joining these concepts, they proposed a Collaborative Deep Learning (CDL).…”

Section: Evaluation Of Each Hotel's Neighboring Natural Environ-mentioning

confidence: 99%

“…Since the training of Neural Network remains a striving task, the available explicit models not effectively utilized the potential of deep learning. Qibing Li et.al [39], in 2018 focused on work to defend these challenges. They focused on developing a standard recommender system named Neural Collaborative Autoencoder (NCAE) to perform collaborative filtering, which works well for both explicit feedback and implicit feedback.…”

Section: Evaluation Of Each Hotel's Neighboring Natural Environ-mentioning

confidence: 99%

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The evolution of travel recommender systems: A comprehensive review

Muneer¹,

Basheer²

2020

Malaya J. Mat

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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.

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Section: Evaluation Of Each Hotel's Neighboring Natural Environ-mentioning

confidence: 99%

Section: Evaluation Of Each Hotel's Neighboring Natural Environ-mentioning

confidence: 99%

The evolution of travel recommender systems: A comprehensive review

Muneer¹,

Basheer²

2020

Malaya J. Mat

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“…For generating recommendation finally a singular value decomposition (SVD) algorithm is used to find the preferences. Ajantha et al [15] worked on a system that can help the tourism services. They proposed a system using the user-location vector to identify the relationship between POIs and user interest.…”

Section: Place Recommendation Systemmentioning

confidence: 99%

An Empirical Recommendation Framework to Support Location-Based Services

et al. 2020

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The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.

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“…Kullanıcı-konum vektörleri ise konum bilgileri ve kullanıcı profil bilgilerine göre hesaplanmaktadır. Konum profil modülü ise kullanıcı profillerinden çıkarılan kullanıcıların ilgi alanları ile yakın kullanıcıların ilgi alanlarına göre ziyaret ettikleri konumları eşleştirmektedir [69].…”

Section: Büyük Veri̇ Ve Tavsi̇ye Si̇stemleri̇unclassified

Tavsiye Sistemlerinde Büyük Verinin Kullanımı Üzerine Kapsamlı Bir İnceleme

Utku

Akçayol

2018

Marmara Fen Bilimleri Dergisi

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Web tabanlı e-ticaret platformlarındaki gelişmeler, tavsiye sistemlerinin giderek önem kazanmasına neden olmaktadır. Tavsiye sistemleri, kullanıcılar için faydalı ve kişiselleştirilmiş öneriler sunmak için geliştirilen sistemlerdir. Büyük veri çağında, artan sayıda kullanıcı ve ürün karşısında mevcut tavsiye sistemleri ölçeklenebilirlik ve verimlilik sorunları yaşamaktadır. Bu çalışma kapsamında, büyük veri ve tavsiye sistemleri üzerine kapsamlı ve karşılaştırmalı bir inceleme yapılmıştır. Literatürde büyük verinin tavsiye sistemlerinde kullanıldığı çalışmalar incelenmiş, büyük verinin tavsiye sistemlerine yüksek performans ve başarı ile uygulanabilmesi için gerekli önişlemler ve yöntemler detaylı bir şekilde incelenmiştir.

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A user-location vector based approach for personalised tourism and travel recommendation

Cited by 17 publications

References 7 publications

The evolution of travel recommender systems: A comprehensive review

The evolution of travel recommender systems: A comprehensive review

An Empirical Recommendation Framework to Support Location-Based Services

Tavsiye Sistemlerinde Büyük Verinin Kullanımı Üzerine Kapsamlı Bir İnceleme

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