nach oben

Journal of Intelligent Information Systems

Erschienen in:

17.06.2019

Context-aware user preferences prediction on location-based social networks

verfasst von: Fan Wang, Xiangwu Meng, Yujie Zhang

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 1/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Recently, the increasing number of mobile users in location-based social networks (LBSNs) has generated large amounts of data, which provides unprecedented opportunities to study mobile user preferences for location recommendation. However, the huge amount of LBSNs data and sparsity problem limited improvements of efficiency and accuracy on mobile user preferences for location recommendation. This paper proposes a context-aware user preferences prediction algorithm for location recommendation on LBSNs. It introduces cloud model and category information into estimating the similarity of users and locations. Furthermore, it predicts user preferences of new locations according to the categories of new locations and user visited. In particular, the algorithm is parallelized with MapReduce framework for significant improvement in efficiency. Experimental results on Foursquare dataset demonstrate the performance gains of the algorithm.

Vorheriger Artikel Efficient question classification and retrieval using category information and word embedding on cQA services

Nächster Artikel Clustering of semantically enriched short texts

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Amdahl, G.M. (1967). Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18–20, 1967, spring joint computer conference (pp. 483–485). ACM.

Bagci, H., & Karagoz, P. (2016). Context-aware location recommendation by using a random walk-based approach. Knowledge and Information Systems, 47(2), 241–260.CrossRef

Bao, J., Yu, Z.M.F., Formoso, V, Fernández, D, Cacheda, F, Carneiro, V. (2015). Distributed architecture for k-nearest neighbors recommender systems. World Wide Web, 18(4), 997–1017.CrossRef

Bao, J., Zheng, Y., Wilke, D., Mokbel, M.F. (2014). A survey on recommendations in location-based social networks. Submitted to GeoInformatica.

Capdevila, J., Arias, M., Arratia, A. (2016). Geosrs : a hybrid social recommender system for geolocated data. Information Systems, 57, 111–128.CrossRef

Chen, C., Haiqin, Y., Irwin, K., Michael, R.L. (2012). Fused matrix factorization with geographical and social influence in location-based social networks. In: Proceedings of the 26th AAAI conference on artificial intelligence (pp. 17–23).

Dequan, Z., & Xin, W. (2014). Probabilistic category-based location recommendation utilizing temporal influence and geographical influence. In: International Conference on Data Science and Advanced analytics, DSAA 2014, Shanghai, China, October 30 - November 1, 2014 (pp. 115–121).

Faber, D.S., & Korn, H. (1991). Applicability of the coefficient of variation method for analyzing synaptic plasticity. Biophysical Journal, 60(5), 1288–94.CrossRef

Formoso, V., Fernández, D., Cacheda, F., Carneiro, V. (2015). Distributed architecture for k-nearest neighbors recommender systems. World Wide Web, 18(4), 997–1017.CrossRef

Gao, H., Tang, J., Liu, H. (2012). 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).

Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F. (2012). Lars: A location-aware recommender system. In: 2012 IEEE 28th international conference on data engineering (ICDE) (pp. 450–461).

Jeffrey, D., & Sanjay, G. (2004). Mapreduce: simplified data processing on large clusters. Communications of the Acm, 51(1), 107–113.

Kumar, V., & Singh, V. (1991). Scalability of parallel algorithms for the all-pairs shortest-path problem. Journal of Parallel and Distributed Computing, 13(2), 124–138.CrossRef

Li, D., & Du, Y. (2007). Artificial Intelligence with Uncertainty. Boca Raton: CRC Press.CrossRefMATH

Li, D., Liu, C., Gan, W. (2009). A new cognitive model: Cloud model. International Journal of Intelligent Systems, 24(3), 357–375.CrossRefMATH

Robertson, S. (2004). Understanding inverse document frequency: on theoretical arguments for idf, Journal of documentation, (Vol. 60 pp. 503–520).

Tobler, W.R. (1970). A computer movie simulating urban growth in the detroit region. Geospatial Semantics First International Conference Geos, 46, 234–240.

Xu, G., Fu, B., Gu, Y. (2016). Point-of-interest recommendations via a supervised random walk algorithm. Intelligent Systems IEEE, 31(1), 15–23.CrossRef

Xuefeng, C., Yifeng, Z., Gao, C., Shengchao, Q., Yanping, X., Yuanshun, D. (2015). On information coverage for location category based point-of-interest recommendation. In: Twenty-ninth AAAI conference on artificial intelligence (pp. 37–43).

Ye, M., Yin, P., Lee, W.C. (2010). Location recommendation for location-based social networks. In: Gis Proceedings of Sigspatial International Conference on Advances in Geographic Inform (pp. 458–461).

Ye, M., Yin, P., Lee, W.C., Lee, D.L. (2011). 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).

Ye, M., Liu, X., Lee, W.C. (2012). Exploring social influence for recommendation-a probabilistic generative model approach. In: In annual international ACM SIGIR conference on research and development in information retrieval (pp. 325–334).

Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C. (2015). Modeling location-based user rating profiles for personalized recommendation. Acm Transactions on Knowledge Discovery from Data, 9(3), 1–41.CrossRef

Zhang, J.D., & Chow, C.Y. (2013). igslr: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 334–343).

Zhang, J.D., & Chow, C.Y. (2015). Core: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations. Information Sciences, 293, 163–181.CrossRef

Zhang, J.D., Chowmember, C.Y., Li, Y. (2015). igeorec: A personalized and efficient geographical location recommendation framework. IEEE Transactions on Services Computing, 8(5), 701–714.CrossRef

Titel: Context-aware user preferences prediction on location-based social networks
verfasst von: Fan Wang
Xiangwu Meng
Yujie Zhang
Publikationsdatum: 17.06.2019
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 1/2019
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-019-00563-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2019

AWML: adaptive weighted margin learning for knowledge graph embedding

Collaborative filtering recommendation based on trust and emotion

Clustering of semantically enriched short texts

Activity prediction in process mining using the WoMan framework

EVE: explainable vector based embedding technique using Wikipedia

Real-time recommendation with locality sensitive hashing