Title: Spatial Item Recommendation using collaborative filtering

Author(s):  1Yuvraj D. Chougale, 2Mr. B. S. Satpute

 DOI : https://dx.doi.org/10.18535/ijecs/v6i7.23


1Student, Dept. of Computer Science,

Dr. D. Y. Patil Institute of Technology, Pimpri, Pune-18.

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2Assistant Professor. Dept. of Computer Science,

Dr. D. Y. Patil Institute of Technology, Pimpri, Pune-18.

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Abstract

In the development of location-based social networks (LBSNs), spatial items have recommended and that has become an important way of helping users discover interesting locations to increase their engagement with location-based services. Although human movement exhibits sequential patterns in LBSNs, most current studies on spatial item recommendations do not consider the sequential influence of locations. we propose a sequential personalized spatial item recommendation framework (SPORE) which introduces a novel latent variable topic-region to model and fuse sequential influence with personal interest in the latent and exponential space. The advantages of modeling the sequential effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity and a direct expression of the semantic meaning of users’ spatial activities. We evaluate the performance of SPORE on two real datasets and one large-scale synthetic dataset. The results demonstrate a significant improvement in SPORE’s ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods

 


License: This work is licensed under a Creative Commons Attribution 4.0 International License.

 

Website: http://www.ijecs.in

e-ISSN:  2319-724

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