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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (5): 77-88    DOI: 10.11925/infotech.2096-3467.2021.1047
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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).

Key wordsSpectral Clustering      Point of Interests Recommendation      Location Based Social Network     
Received: 16 September 2021      Published: 21 June 2022
ZTFLH:  TP391  
Fund:Natural Science Foundation of Tianjin(19JCYBJC15800);Key Project Foundation of Tianjin(15ZXHLGX003901);National Natural Science Foundation of China(61702366)
Corresponding Authors: Wang Ze,ORCID: 0000-0001-6971-2004     E-mail: wangze@tiangong.edu.cn

Cite this article:

Guo Lei, Liu Wenju, Wang Ze, Ren Yueqiang. Point-of-Interest Recommendation with Spectral Clustering and Multi-Factors. Data Analysis and Knowledge Discovery, 2022, 6(5): 77-88.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1047     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I5/77

LBSN Network
User Check-in Sequential Pattern
比较项 Foursqure Gowalla
签到记录数 4 648 106 2 688 134
用户数 18 828 7 272
兴趣点数 167 310 62 764
好友关系数 59 254 80 838
兴趣点种类数 229 177
Basic Information of the Datasets
Precision and Recall of POI Recommendation on Gowalla
Precision and Recall of POI Recommendation on Foursquare
F1 of POI Recommendation
Three Algorithms Operation Time
Influences of Different Factors on Precision
Influences of Different Factors on Recall
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