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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 9/2020

20.05.2020 | Regular Paper

TAILOR: time-aware facility location recommendation based on massive trajectories

verfasst von: Zhixin Qi, Hongzhi Wang, Tao He, Chunnan Wang, Jianzhong Li, Hong Gao

Erschienen in: Knowledge and Information Systems | Ausgabe 9/2020

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Abstract

In traditional facility location recommendations, the objective is to select the best locations which maximize the coverage or convenience of users. However, since users’ behavioral habits are often influenced by time, the temporal impacts should not be neglected in recommendation. In this paper, we study the problem of time-aware facility location recommendation problem, taking the time factor into account. To solve this problem, we develop a framework, TAILOR, which incorporates the temporal influence, user-coverage, and user-convenience. Based on TAILOR, we derive a greedy algorithm with (1-\(\frac{1}{e}\))-approximation and an online algorithm with (\(\frac{1}{4}\))-competitive ratio. Extensive experimental evaluation and two case studies demonstrate the efficiency and effectiveness of the proposed approaches.
Vorheriger Artikel Rule extraction from neural network trained using deep belief network and back propagation
Nächster Artikel PragmaticOIE: a pragmatic open information extraction for Portuguese language

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Literatur
1.
Ausiello G, Boria N, Giannakos A, Lucarelli G, Paschos VT (2012) Online maximum k-coverage. Discrete Appl Math 160(13–14):1901–1913MathSciNetCrossRef
2.
Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th international conference on advances in geographic information systems, pp 199–208
3.
Berman O, Krass D (1998) Flow intercepting spatial interaction model: a new approach to optimal location of competitive facilities. Locat Sci 6(1–4):41–65CrossRef
4.
5.
Berman O, Larson RC, Fouska N (1992) Optimal location of discretionary service facilities. Transp Sci 26(3):201–211CrossRef
6.
Berman O, Krass D, Xu CW (1995) Locating discretionary service facilities based on probabilistic customer flows. Transp Sci 29(3):276–290CrossRef
7.
Boccia M, Sforza A, Sterle C (2009) Flow intercepting facility location: problems, models and heuristics. J Math Model Algorithms 8(1):35–79MathSciNetCrossRef
8.
Chen Z, Liu Y, Wong RCW, Xiong J, Mai G, Long C (2014) Efficient algorithms for optimal location queries in road networks. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp 123–134
9.
Ding Y, Li X (2005) Time weight collaborative filtering. In: Proceedings of the 14th ACM international conference on Information and knowledge management, pp 485–492
10.
Drezner Z, Hamacher HW (2001) Facility location: applications and theory. Springer, BerlinMATH
11.
Du Y, Zhang D, Xia T (2005) The optimal-location query. In: International symposium on spatial and temporal databases, pp 163–180
12.
Ghaemi P, Shahabi K, Wilson JP, Banaei-Kashani F (2010) Optimal network location queries. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 478–481
13.
Guo L, Zhang D, Cong G, Wu W, Tan KL (2017) Influence maximization in trajectory databases. IEEE Trans Knowl Data Eng 29(3):627–641CrossRef
14.
Hodgson MJ (1981) The location of public facilities intermediate to the journey to work. Eur J Oper Res 6(2):199–204CrossRef
15.
Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 447–456
16.
Li D, Li H, Wang M, Cui J (2019) k-collective influential facility placement over moving object. In: 2019 20th IEEE international conference on mobile data management, pp 191–200
17.
Li X, Čeikute V, Jensen CS, Tan KL (2013) Trajectory based optimal segment computation in road network databases. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, pp 396–399
18.
Li Y, Bao J, Li Y, Wu Y, Gong Z, Zheng Y (2018) Mining the most influential \( k \)-location set from massive trajectories. IEEE Trans Big Data 4(4):556–570CrossRef
19.
Lou Y, Zhang C, Zheng Y, Xie X, Wang W, Huang Y (2009) Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 352–361
20.
Mitra S, Saraf P, Sharma R, Bhattacharya A, Ranuy S, Bhandari H (2017) Netclus: a scalable framework for locating top-k sites for placement of trajectory-aware services. In: Proceedings of the 33rd international conference on data engineering, pp 87–90
21.
Mitra S, Saraf P, Bhattacharya A (2019) Tips: mining top-k locations to minimize user-inconvenience for trajectory-aware services. IEEE Trans Knowl Data Eng
22.
Mustafizur A, Khan R, Kulik L, Tanin E, Hashem T (2015) Optimal mobile facility localization. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, pp 1–4
23.
Nemhauser GL, Wolsey LA, Fisher ML (1978) An analysis of approximations for maximizing submodular set functions—i. Math Program 14(1):265–294MathSciNetCrossRef
24.
Saha B, Getoor L (2009) On maximum coverage in the streaming model & application to multi-topic blog-watch. In: Proceedings of the 2009 SIAM international conference on data mining, pp 697–708
25.
Saranya C, Manikandan G (2013) A study on normalization techniques for privacy preserving data mining. Int J Eng Technol 5(3):2701–2704
26.
Wang M, Li H, Cui J, Deng K, Bhowmick SS, Dong Z (2017) Pinocchio: probabilistic influence-based location selection over moving objects. In: 2017 IEEE 33rd international conference on data engineering, pp 21–22
27.
Wei LY, Zheng Y, Peng WC (2012) Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 195–203
28.
Xiang L, Yuan Q, Zhao S, Chen L, Zhang X, Yang Q, Sun J (2010) Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 723–732
29.
Xiao X, Yao B, Li F (2011) Optimal location queries in road network databases. In: 2011 IEEE 27th international conference on data engineering, pp 804–815
30.
Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp 363–372
31.
Zhang D, Guo L, Nie L, Shao J, Wu S, Shen HT (2017) Targeted advertising in public transportation systems with quantitative evaluation. ACM Trans Inf Syst 35(3):1–29
32.
Zhang P, Bao Z, Li Y, Li G, Zhang Y, Peng Z (2018) Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2748–2757
33.
Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on ubiquitous computing, pp 312–321
34.
Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on world wide web, pp 791–800
35.
Zheng Y, Xie X, Ma WY et al (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39
Metadaten
Titel
TAILOR: time-aware facility location recommendation based on massive trajectories
verfasst von
Zhixin Qi
Hongzhi Wang
Tao He
Chunnan Wang
Jianzhong Li
Hong Gao
Publikationsdatum
20.05.2020
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 9/2020
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-020-01477-w

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