Prediction Based on Point-of-Interest Recommendation Algorithm in LBSN

DUAN Zong-tao; CAI Dan-dan; TANG Lei; LI Fei-fei

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Prediction Based on Point-of-Interest Recommendation Algorithm in LBSN

更新时间：2026-09-04

- Prediction Based on Point-of-Interest Recommendation Algorithm in LBSN
- Vol. 36, Issue 1, Pages: 66-69(2019)
- 作者机构：
  
  长安大学信息工程学院,陕西,西安,710064
- 作者简介：
- 基金信息：
- DOI：
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- Published：2019
- 稿件说明：
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段宗涛, 蔡丹丹, 唐蕾, 李菲菲. 基于预测的LBSN兴趣点推荐算法[J]. 微电子学与计算机, 2019, 36(1): 66-69.

DUAN Zong-tao, CAI Dan-dan, TANG Lei, et al. Prediction Based on Point-of-Interest Recommendation Algorithm in LBSN[J]. 2019, 36(1): 66-69.
段宗涛, 蔡丹丹, 唐蕾, 李菲菲. 基于预测的LBSN兴趣点推荐算法[J]. 微电子学与计算机, 2019, 36(1): 66-69. DOI：

DUAN Zong-tao, CAI Dan-dan, TANG Lei, et al. Prediction Based on Point-of-Interest Recommendation Algorithm in LBSN[J]. 2019, 36(1): 66-69. DOI：

摘要

兴趣点(Point-Of-Interest

POI)推荐是基于位置的社交网络中(Location-Based Social Networks

LBSN)一种重要的个性化推荐功能.本文提出基于预测的兴趣点推荐算法.该算法根据LBSN中用户历史POI数据分布学习用户出行行为

利用变阶的马尔科夫算法根据当前位置预测用户未来到达POI的语义信息

最终推荐时考虑用户签到次数的差异为用户推荐个具有高兴趣度的POI.实验结果表明:本文提出的算法在准确率和召回率上均高于两个对比算法

说明该算法提高了兴趣点推荐效果

并可以有效的推荐给用户下一个访问的兴趣点.

Abstract

Point-Of-Interest (POI) recommendation is an important personalized recommendation function in Location-Based Social Networks (LBSN). This paper proposes a prediction point based recommendation algorithm. The algorithm learns the users travel behavior according to the user history POI data distribution in the LBSN

and uses the variable Markov algorithm to predict the semantic information of the users future arrival POI according to the current location. The final recommendation takes into account the difference in the number of user sign-ups and recommends for the user. Highly interesting POI. The experimental results show that the proposed algorithm is higher than the two comparison algorithms in accuracy and recall rate

which indicates that the algorithm improves the recommendation effect of interest points and can effectively recommend the next interest points for users.

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references

Ference G, Ye M, Lee W C.Location recommendation for out-of-town users in location-based social network[C]//Proc of the 22nd ACM International Conference on Information % Knowledge Management. SanFrancisco, 2013: 721-726.

Mao Ye, Yin Peifeng, Wang C L. Location recommendation for location-based social networks[C]//Proc of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York: ACM Press, 2010: 458-461.

Deshpande M, Karypis G. Item-based top- N, recommendation algorithms[J]. Acm.Transactions on Information Systems, 2004, 22(1):143-177. DOI: 143

Ye M, Yin P, Lee W C, et al. Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. Tokyo, Japan, 2011: 325-334.

Shi Yue, Serdyukov P, Hanjalic A, et al. Personalized landmark recommendation based on geotags from photo sharing sites[C]// Proc of ICWSM. Barcelona, Spain, 2011: 622-625.

Yoshii K, Goto M, Komatani K, et al. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model[J]. IEEE Transactionson Audio Speech % Language Processing, 2008, 16(2):435-447. DOI: 435

Sen Wang. A location recommendation algorithm based on location-based social networks [J].Computer Engineering% Science, 2016, 38(4):1007-130. DOI: 1007

Berjani B, Strufe T. A recommendation system for spots in location-based online social networks[C]//Proc of the 4th Workshop on Social Network Systems. NewYork: ACM Press, 2011: 4-20.

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