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| Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors |
Guo Lei,Liu Wenju,Wang Ze( ),Ren Yueqiang |
| College of Computer Science and Technology, Tiangong University, Tianjin 300387, China |
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Abstract [Objective] This paper tries to improve the recommendation algorithm for Location-Based Social Networks (LBSN) and reduce the impacts of sparse data on recommendation precision. [Methods] First, we used the adaptive spectral clustering technique to group the users. Then, we created the recommending candidates for the point of interests (POIs) visited by the users. Finally, we calculated the attracting scores of the candidate sets and generated the recommended POIs with higher scores. [Results] We examined the new model with two real LBSN data sets: Gowalla and Foursquare, and set the recommended number of POIs as 2. Our model’s precision reached 11.4% and 7.4%, which were 3.2% and 1.1% higher than the Lore model. The new model’s running time reduced to 50 644.53 s and 406 224.7 s (16 961.49 s and 227 248.6 s shorter than the benchmark model). [Limitations] The clustering algorithm could influence the screening of POIs. [Conclusions] The proposed model could effectively improve the recommendation precision of heterogeneous networks (i.e.,LBSN).
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Received: 16 September 2021
Published: 21 June 2022
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| Fund:Natural Science Foundation of Tianjin(19JCYBJC15800);Key Project Foundation of Tianjin(15ZXHLGX003901);National Natural Science Foundation of China(61702366) |
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Corresponding Authors:
Wang Ze,ORCID： 0000-0001-6971-2004
E-mail: wangze@tiangong.edu.cn
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