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TPM: A Temporal Personalized Model for Spatial Item Recommendation by Weiqing Wang; Hongzhi Yin; Xingzhong Du; Quoc Viet Hung Nguyen; Xiaofang Zhou is a Computer Science article available to read on EtoBox.

What is TPM: A Temporal Personalized Model for Spatial Item Recommendation about?

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction targe

Who reads TPM: A Temporal Personalized Model for Spatial Item Recommendation?

Author

Weiqing Wang; Hongzhi Yin; Xingzhong Du; Quoc Viet Hung Nguyen; Xiaofang Zhou

Publisher

Association for Computing Machinery; Association for Computing Machinery (ACM) (ISSN 2157-6904)

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)