Proceedings ArticleDOI
Location-Time-Sociality Aware Personalized Tourist Attraction Recommendation in LBSN
Ziqing Zhu,Jiuxin Cao,Chenghao Weng +2 more
- pp 636-641
TLDR
A personalized tourist attraction recommendation mechanism is designed in terms of different factors, including user preference, social relationship, location distance and location popularity, which ranks tourist attractions based on weighted average by combining the above three scores.Abstract:
With the development of the tourism, vast amount of tourist attraction information makes it complex and time-consuming for users to obtain satisfactory travel destination Rich topological, temporal and spatial information in Location-Based Social Network (LBSN) helps to mine user preference deeply and evokes effectiveness of tourist attraction recommendation In this paper, a personalized tourist attraction recommendation mechanism is designed in terms of different factors, including user preference, social relationship, location distance and location popularity Firstly, a large number of check-ins in LBSN are filtered for further geographical space clustering to get real tourist attraction check-ins by DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm Then, a location-aware LDA model is used to excavate user's latent preference topic distribution and region's latent topic distribution on tourist attractions After that, these two attributes together with social relationship are used to make user interest scoring function Furthermore, another two scoring functions are established respectively based on tourist attraction location distance and popularity At last, a personalized tourist attraction recommendation algorithm is designed to recommend tourist attractions to users, which ranks tourist attractions based on weighted average by combining the above three scores Experiments are carried out on Foursquare data sets and results show that our method can perform well on recommendation in home city and new cityread more
Citations
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Journal ArticleDOI
A Comprehensive Survey on Travel Recommender Systems
Kinjal Chaudhari,Ankit Thakkar +1 more
TL;DR: This survey believes it would introduce a state-of-the-art travel recommender system (RS) and may be utilized to solve the existing limitations and extend its applicability.
Journal ArticleDOI
Point of interest recommendations based on the anchoring effect in location-based social network services
Young-Duk Seo,Yoon-Sik Cho +1 more
TL;DR: This paper proposes a latent Dirichlet allocation (LDA) model based on the anchoring effect for POI RS that emphasizes the importance of initial check-in data and is called the anchor-LDA, and experimental results showed that this model outperformed existing LDA-based POI recommender algorithms.
Journal ArticleDOI
Personalized Recommendation Algorithm of Tourist Attractions Based on Transfer Learning
TL;DR: The findings of comparative trials suggest that the personalized scenic location recommendation approach proposed in this study, which is based on the depth migration algorithm, is effective and the recommendation accuracy and recall rate has been improved to a certain extent.
Journal ArticleDOI
Geographical index of concentration as an indicator of the spatial distribution of tourist attractions in Belgrade
Sanja Pavlović,Radmila Jovanović +1 more
TL;DR: In this article, the authors used field research, OSM (Open Street Maps), Google maps, with software processing ArcGIS 10.2 to determine the spatial distribution of tourist attractions in the administrative territories of Belgrade municipalities and to establish correlations with tourist attendance.
Proceedings ArticleDOI
Data driven user feature prediction in mobile applications based on multi-channel CNN
TL;DR: A muti-dimensional user feature construction method is proposed, which extracts user feature from different influencing factors and can promote the transformation from user data to user feature and help solve the problem of “explosive data but poor knowledge”.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
Location recommendation for location-based social networks
TL;DR: A friend-based collaborative filtering approach for location recommendation based on collaborative ratings of places made by social friends is developed, and a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset is proposed.
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