Reliable Collaborative Filtering on Spatio-Temporal Privacy Data
Joint Authors
Liu, Zhen
Meng, Huanyu
Ren, Shuang
Liu, Feng
Source
Security and Communication Networks
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-28
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN).
When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information.
The existing approaches of collaborative filtering use only the sparse user-item rating matrix.
It entails high computational complexity and inaccurate results.
A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper.
By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed.
Then the clustering algorithm is used to obtain and narrow the set of similar users.
User-location bipartite graph is modeled using the filtered similar user set.
Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph.
Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations.
Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.
American Psychological Association (APA)
Liu, Zhen& Meng, Huanyu& Ren, Shuang& Liu, Feng. 2017. Reliable Collaborative Filtering on Spatio-Temporal Privacy Data. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1203204
Modern Language Association (MLA)
Liu, Zhen…[et al.]. Reliable Collaborative Filtering on Spatio-Temporal Privacy Data. Security and Communication Networks No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1203204
American Medical Association (AMA)
Liu, Zhen& Meng, Huanyu& Ren, Shuang& Liu, Feng. Reliable Collaborative Filtering on Spatio-Temporal Privacy Data. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1203204
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203204