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- W2889609968 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 city." @default.
- W2889609968 created "2018-09-27" @default.
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- W2889609968 date "2018-05-01" @default.
- W2889609968 modified "2023-09-26" @default.
- W2889609968 title "Location-Time-Sociality Aware Personalized Tourist Attraction Recommendation in LBSN" @default.
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- W2889609968 doi "https://doi.org/10.1109/cscwd.2018.8465179" @default.
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