Top

World Wide Web

Published in:

02-05-2018

Leveraging multi-aspect time-related influence in location recommendation

Authors: Saeid Hosseini, Hongzhi Yin, Xiaofang Zhou, Shazia Sadiq, Mohammad Reza Kangavari, Ngai-Man Cheung

Published in: World Wide Web | Issue 3/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Point-Of-Interest (POI) recommendation aims to mine a user’s visiting history and find her/his potentially preferred places. Although location recommendation methods have been studied and improved pervasively, the challenges w.r.t employing various influences including temporal aspect still remain unresolved. Inspired by the fact that time includes numerous granular slots (e.g. minute, hour, day, week and etc.), in this paper, we define a new problem to perform recommendation through exploiting all diversified temporal factors. In particular, we argue that most existing methods only focus on a limited number of time-related features and neglect others. Furthermore, considering a specific granularity (e.g. time of a day) in recommendation cannot always apply to each user or each dataset. To address the challenges, we propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) to promote the effectiveness of the location (POI) recommendation task. We also develop an effective optimization algorithm based on Expectation Maximization (EM). Our MATI model firstly detects a user’s temporal multivariate orientation using her check-in log in Location-based Social Networks(LBSNs). It then performs recommendation using temporal correlations between the user and proposed locations. Our method is applicable to various types of the recommendation models and can work efficiently in multiple time-scales. Extensive experimental results on two large-scale LBSN datasets verify the effectiveness of our method over other competitors. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Information filtering; H.2.8 [Database Applications]: Data mining; J.4 [Computer Applications]: Social and Behavior Sciences

previous article Searching activity trajectory with keywords

next article CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

A temporal slot, scale, and dimension (e.g. Hour, Day and etc.) are used interchangeably in this paper unless noted otherwise.

We created matrices of h*h using LINQ queries in which h is the number of slots in each temporal scale (i.e. 7 for z_d and 24 for z_h).

http://www.public.asu.edu/~hgao16/

https://snap.stanford.edu/data/loc-brightkite.html

We used Microsoft SQL Server 2012 relational databases. In expense of the disk space, both non-clustered and clustered indexes which were advised via Microsoft SQL Server Profiler accelerated the process speed exceptionally.

Bao, J., Zheng, Y, Wilkie, D., Mokbel, M.F.: A Survey on Recommendations in Location-Based Social Networks. Submitted to GeoInformatica (2014)

Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

Cheng, C., Yang, H., Lyu, M.R., next, I. King.: Where you like to go Successive point-of-interest recommendation. In: IJCAI, vol. 13, pp. 2605–2611 (2013)

Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: User movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090 ACM (2011)

Das, M., Thirumuruganathan, S., Amer-Yahia, S., Das, G., Yu, C.: An expressive framework and efficient algorithms for the analysis of collaborative tagging. VLDB J. 23(2), 201–226 (2014)CrossRef

Deveaud, R., Albakour, M.-D., Macdonald, C., Ounis, I.: Experiments with a venue-centric model for personalisedand time-aware venue suggestion. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 53–62. ACM (2015)

Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 485–492. ACM (2005)

Fang, Q., Xu, C., Hossain, M.S., Muhammad, G.: Stcaplrs: A spatial-temporal context-aware personalized location recommendation system. ACM Transa. Intell. Syst. Technol. (TIST) 7(4), 59 (2016)

Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 93–100. ACM (2013)

10.

Gao, H., Tang, J., Liu, H: gscorr: Modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1582–1586. ACM (2012)

11.

Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)

12.

Hosseini, S., Li, L.T.: Point-of-interest recommendation using temporal orientations of users and locations. In: International Conference on Database Systems for Advanced Applications, pp. 330–347. Springer (2016)

13.

Hu, B., Ester, M.: Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 25–32. ACM (2013)

14.

Hung, C.-C., Peng, W.-C., Lee, W.-C.: Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J., 1–24 (2011)

15.

Leung, K. W.-T., Lee, D.L., Lee, W.-C.: Clr: A collaborative location recommendation framework based on co-clustering. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 305–314. ACM (2011)

16.

Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F.: Lars: A location-aware recommender system. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 450–461. IEEE (2012)

17.

Li, X., Xu, G., Chen, E., Zong, Y.: Learning recency based comparative choice towards point-of-interest recommendation. Expert Syst. Appl. 42(9), 4274–4283 (2015)CrossRef

18.

Li, H., Ge, Y., Hong, R., Zhu, H.: Point-of-interest recommendations: Learning potential check-ins from friends. In: KDD, pp. 975–984 (2016)

19.

Lian, D., Zhang, Z., Ge, Y., Zhang, F., Yuan, N.J., Xie, X.: Regularized content-aware tensor factorization meets temporal-aware location recommendation. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1029–1034. IEEE (2016)

20.

Liberty, E., Lang, K., Shmakov, K.: Stratified sampling meets machine learning. In: Proceedings of The 33rd International Conference on Machine Learning, pp. 2320–2329 (2016)

21.

Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 733–738. ACM (2013)

22.

Liu, B., Xiong, H., Papadimitriou, S., Fu, Y., Yao, Z.: A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans. Knowl. Data Eng. 27(5), 1167–1179 (2015)CrossRef

23.

Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefMATH

24.

Rahimi, S.M., Wang, X.: Location recommendation based on periodicity of human activities and location categories. In: Advances in Knowledge Discovery and Data Mining, pp. 377–389. Springer (2013)

25.

Ricci, F., Nguyen, Q.N.: Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intell. Syst. 22(3), 22–29 (2007)CrossRef

26.

Symeonidis, P., Ntempos, D., Manolopoulos, Y.: Location-based social networks. In: Recommender Systems for Location-based Social Networks, pp. 35–48. Springer (2014)

27.

Tax, D.M., Duin, R.P.: Feature scaling in support vector data descriptions. Technical report (2000)

28.

Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications (2006)

29.

Wang, C., Ye, M., Lee, W.-C.: From face-to-face gathering to social structure. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 465–474. ACM (2012)

30.

Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: A geographical sparse additive generative model for spatial item recommendation. arXiv:1503.03650 (2015)

31.

Wang, Y., Yuan, N.J., Lian, D., Xu, L., Xie, X., Chen, E., Rui, Y.: Regularity and conformity: Location prediction using heterogeneous mobility data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1275–1284. ACM (2015)

32.

Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: 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 .ACM (2010)

33.

Ye, M., Liu, X., Lee, W.-C.: Exploring social influence for recommendation: A generative model approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 671–680. ACM (2012)

34.

Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM (2011)

35.

Yin, H., Cui, B.: Location-based and real-time recommendation. In: Spatio-Temporal Recommendation in Social Media, pp. 65–98. Springer (2016)

36.

Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: A location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 221–229. ACM (2013)

37.

Yin, H., Cui, B., Sun, Y., Hu, Z., Chen, L.: Lcars: A spatial item recommender system. ACM Trans. Inf. Syst. (TOIS) 32(3), 11 (2014)CrossRef

38.

Yin, H., Cui, B., Huang, Z., Wang, W., Wu, X., Zhou, X.: Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 819–822. ACM (2015)

39.

Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1631–1640. ACM (2015)

40.

Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N. M.: 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. ACM (2013)

41.

Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Who, where, when and what: discover spatio-temporal topics for twitter users. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 605–613. ACM (2013)

42.

Yuan, Q., Cong, G., Sun, A.: Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 659–668. ACM (2014)

43.

Zhang, J.-D., Chow, C.-Y.: Ticrec: A probabilistic framework to utilize temporal influence correlations for time-aware location recommendations

44.

Zhang, W., Wang, J.: Location and time aware social collaborative retrieval for new successive point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1221–1230. ACM (2015)

45.

Zhang, J.-D., Chow, C.-Y., Li, Y.: igeorec: A personalized and efficient geographical location recommendation framework. IEEE Trans. Serv. Comput. 8(5), 701–714 (2015)CrossRef

46.

Zhang, Y., Zhang, M., Zhang, Y., Lai, G., Liu, Y., Zhang, H., Ma, S.: Daily-aware personalized recommendation based on feature-level time series analysis. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1373–1383. International World Wide Web Conferences Steering Committee (2015)

47.

Zhao, S., King, I., Lyu, M.R.: A survey of point-of-interest recommendation in location-based social networks. arXiv:1607.00647 (2016)

48.

Zhao, S., Zhao, T., King, I., Lyu, M.R.: Gt-seer: Geo-temporal sequential embedding rank for point-of-interest recommendation. arXiv:1606.05859 (2016)

49.

Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)

50.

Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with gps history data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1029–1038. ACM (2010)

51.

Zheng, Y., Zhou, X.: Computing with Spatial Trajectories. Springer Science & Business Media (2011)

Title: Leveraging multi-aspect time-related influence in location recommendation
Authors: Saeid Hosseini
Hongzhi Yin
Xiaofang Zhou
Shazia Sadiq
Mohammad Reza Kangavari
Ngai-Man Cheung
Publication date: 02-05-2018
Publisher: Springer US
Published in: World Wide Web / Issue 3/2019
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-018-0573-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 3/2019

Augmented reality displaying scheme in a smart glass based on relative object positions and orientation sensors

Discovery of accessible locations using region-based geo-social data

Matching user accounts across social networks based on username and display name

Analyzing influence of emotional tweets on user relationships using Naive Bayes and dependency parsing

Guest editorial: special issue on social media and interactive technologies

Self-feeding frequency estimation and eating action recognition from skeletal representation using Kinect

Premium Partner