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Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++
Joint Authors
Li, Guangli
Hua, Jin
Yuan, Tian
Wu, Jinpeng
Jiang, Ziliang
Zhang, Hongbin
Li, Tao
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-17
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Recommendation system for tourist spots has very high potential value including social and economic benefits.
The traditional clustering algorithms were usually used to build a recommendation system.
However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily.
Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference.
To address the problem, we focused our research work on designing a novel recommendation system for tourist spots.
First a new dataset named “Smart Travel” is created for the following experiments.
Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes.
A new recommendation list named LA is generated in turn by fitting the excavated the user preference.
More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings.
And a new recommendation list named LB is generated in turn on the basis of rating predictions.
Finally, the two lists LA and LB are fused together to boost the final recommendation performance.
Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor.
The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.
American Psychological Association (APA)
Li, Guangli& Hua, Jin& Yuan, Tian& Wu, Jinpeng& Jiang, Ziliang& Zhang, Hongbin…[et al.]. 2019. Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1194624
Modern Language Association (MLA)
Li, Guangli…[et al.]. Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++. Mathematical Problems in Engineering No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1194624
American Medical Association (AMA)
Li, Guangli& Hua, Jin& Yuan, Tian& Wu, Jinpeng& Jiang, Ziliang& Zhang, Hongbin…[et al.]. Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1194624
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194624