February  2018, 1(1): 11-48. doi: 10.3934/mfc.2018002

Exploring timeliness for accurate recommendation in location-based social networks

1. 

Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA

2. 

Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA

3. 

School of Computer Science and Technology, Harbin Institute of Technology, Weihai, Shandong 264209, China

Received  September 2017 Revised  November 2017 Published  February 2018

An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.

Citation: Yi Xu, Qing Yang, Dianhui Chu. Exploring timeliness for accurate recommendation in location-based social networks. Mathematical Foundations of Computing, 2018, 1 (1) : 11-48. doi: 10.3934/mfc.2018002
References:
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show all references

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[11]

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[13]

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[15]

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[17]

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[18]

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[19]

Y. Liang, Z. Cai, Q. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, 2017 (2017), Article ID 7576307, 12 pages. doi: 10.1155/2017/7576307.  Google Scholar

[20]

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[21]

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[22]

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[23]

G. Liu, Q. Yang, H. Wang, S. Wu and M. P. Wittie, Uncovering the mystery of trust in an online social network, in Communications and Network Security (CNS), 2015 IEEE Conference on, IEEE, 2015, 488-496. Google Scholar

[24]

C.-T. LuP.-R. LeiW.-C. Peng and I.-J. Su, A framework of mining semantic regions from trajectories, International Conference on Database Systems for Advanced Applications, (2011), 193-207.  doi: 10.1007/978-3-642-20149-3_16.  Google Scholar

[25]

A. Noulas, S. Scellato, C. Mascolo and M. Pontil, Exploiting semantic annotations for clustering geographic areas and users in location-based social networks, Fifth International AAAI Conference on Weblogs and Social Media. Google Scholar

[26]

A. NoulasS. ScellatoN. Lathia and C. Mascolo, Mining user mobility features for next place prediction in location-based services, IEEE 12th International Conference on Data Mining, (2012), 1038-1043.  doi: 10.1109/ICDM.2012.113.  Google Scholar

[27]

I. RheeM. ShinS. HongK. LeeS. Kim Joon and S. Chong, On the levy-walk nature of human mobility, IEEE/ACM Transactions on Networking (TON), (2008).  doi: 10.1109/INFOCOM.2008.145s.  Google Scholar

[28]

L. Rossi and M. Musolesi, It's the way you check-in: Identifying users in location-based social networks, COSN, (2014), 215-226.  doi: 10.1145/2660460.2660485.  Google Scholar

[29]

J. Sang, T. Mei and C. Xu, Activity sensor: Check-in usage mining for local recommendation, ACM Transactions on Intelligent Systems and Technology (TIST), 6 (2015), Article No. 41. doi: 10.1145/2700468.  Google Scholar

[30]

B. SarwarG. KarypisJ. Konstan and J. Riedl, Item-based collaborative filtering recommendation algorithms, WWW '01 Proceedings of the 10th International Conference on World Wide Web, (2001), 285-295.  doi: 10.1145/371920.372071.  Google Scholar

[31]

M. SarwatJ. Levandoski J.A. Eldawy and M. Mokbel, Lars*: An efficient and scalable location-aware recommender system, Transactions ON Knowledge and Data Engineering, 26 (2014), 1384-1399.  doi: 10.1109/TKDE.2013.29.  Google Scholar

[32]

X. Su and K. Taghi M., A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009 (2009), Article ID 421425, 19 pages. doi: 10.1155/2009/421425.  Google Scholar

[33]

Z. SuY. Hui and Q. Yang, The next generation vehicular networks: A content-centric framework, IEEE Wireless Communications, 24 (2017), 60-66.  doi: 10.1109/MWC.2017.1600195WC.  Google Scholar

[34]

Z. SuQ. XuF. HouQ. Yang and Q. Qi, Edge caching for layered video contents in mobile social networks, IEEE Transactions on Multimedia, 19 (2017), 2210-2221.  doi: 10.1109/TMM.2017.2733338.  Google Scholar

[35]

Y. WangG. YinZ. CaiY. Dong and H. Dong, A trust-based probabilistic recommendation model for social networks, Journal of Network and Computer Applications, 55 (2015), 59-67.  doi: 10.1016/j.jnca.2015.04.007.  Google Scholar

[36]

Y. WangN. Yuan JingD. LianL. XuX. XieE. Chen and Y. Rui, Regularity and conformity: Location prediction using heterogeneous mobility data, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 1275-1284.  doi: 10.1145/2783258.2783350.  Google Scholar

[37]

J. Weston, C. Wang, R. Weiss and A. Berenzweig, Latent collaborative retrieval, Proceedings of the 29th International Conference on Machine Learning, 9-16. Google Scholar

[38]

X. XiaoQ. ZhengYu andLuo and X. Xie, Finding similar users using category-based location history, GIS, (2010), 442-445.  doi: 10.1145/1869790.1869857.  Google Scholar

[39]

L. XiongX. ChenT.-K. HuangJ. Schneider G and G. Carbonell, Temporal collaborative filtering with bayesian probabilistic tensor factorization, Siam International Conference on Data Mining, (2010), 211-222.  doi: 10.1137/1.9781611972801.19.  Google Scholar

[40]

D. Yang, D. Zhang, V. Zheng W and Z. Yu, Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 129-142. doi: 10.1109/GLOCOM.2010.5684166.  Google Scholar

[41]

Q. Yang, A. Lim, X. Ruan and X. Qin, Location privacy protection in contention based forwarding for vanets, in Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, IEEE, 2010, 1-5. doi: 10.1109/GLOCOM.2010.5684166.  Google Scholar

[42]

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Figure 1.  Overview a location-based social network
Figure 2.  Influence of training size to the precision
Figure 3.  Cosine similarity between the transition and similarity matrix
Figure 4.  Average number of new visited location changes through each month
Figure 5.  Cumulative distribution function (CDF) of time interval between timelinesss
Figure 6.  Precision and recall of CF with or without K-means clustering and SVD
Figure 7.  Influence of data sparsity and category to the recommendation rate (recall ratio) where number of recommendations $k = 20$
Figure 8.  Influence of constant and dynamic similarity to the recommendation rate (precision ratio) where $N$ is number of multiplications
Figure 9.  Recommendation timeliness comparison for constant similarity matrix and dynamic similarity matrix
Figure 10.  Similarity of the eigenvector of transition matrix and similarity matrix comparisons of our method and the three benchmarks
Figure 11.  The recommendation rate of our method and the three baseline varying in the recommendation timeliness (month)
Figure 12.  The empirical CDF of time intervals of our method and the three benchmarks except CF varying in the recommendation period time (month)
Figure 13.  Precisions and recalls of LAR, CF, LGS and GeoSoCa
Table 1.  An example of user-item matrix
Apple Pear Grape Watermelon
Alice Like Like Dislike Dislike
Bob Dislike Like Like
Chris Dislike Like
Tony Like Dislike
Apple Pear Grape Watermelon
Alice Like Like Dislike Dislike
Bob Dislike Like Like
Chris Dislike Like
Tony Like Dislike
Table 2.  A summary of precision and recall ratios of the four approaches
Models Precision Recall Avg.Precision Avg.Recall
CF 0.51 0.69 0.3573 0.6395
LGS 0.59 0.77 0.3682 0.6762
GeoSoCa 0.58 0.76 0.3657 0.6684
LAR 0.62 0.83 0.4582 0.7523
Models Precision Recall Avg.Precision Avg.Recall
CF 0.51 0.69 0.3573 0.6395
LGS 0.59 0.77 0.3682 0.6762
GeoSoCa 0.58 0.76 0.3657 0.6684
LAR 0.62 0.83 0.4582 0.7523
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