Skip to main content
SpringerLink
Log in

Quality models for venue recommendation in location-based social network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With the rapid development of location-based social networks (LBSNs), increasing media data is ceaselessly uploaded by users. The multimedia data is often scattered and not informative and consequently they can not directly represent the semantics of each venue. Most of prior works leverage the user’ travelling histories to recommend new venues to users. However, these works often focus on the users’ travelling histories, while ignore the concepts or the popular levels of venues. In this paper, we proposed a quality model for venue recommendation by utilizing multimedia data to predict the interested level of each venue. First, we apply the graph cut method to generate the latent textual topics. Second, we leverage visual data from Flickr to train concept detectors to automatically label visual information. Third, the weighted bipartite matching algorithm is implemented to generate the venue multimedia topics by bridging the textual information and the visual information. Finally, we utilize the matching cost to predict the popular level of venue for recommendation. The experiments have been conducted on the cross-platform datasets. The results demonstrate the superiority of the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://www.wikitravel.com

  2. http://www.triptravel.com

References

  1. Alonso O, Gertz M, Baeza-Yates RA, 2010 Temporal analysis of document collections: framework and applications. In: SPIRE, pp 290–296

  2. Amatriain X (2013) Beyond data: from user information to business value through personalized recommendations and consumer science. In: CIKM, pp 2201–2208

  3. Anwar H, Zambanini S, Kampel M (2013) A bag of visual words approach for symbols-based coarse-grained ancient coin classification. CoRR, arXiv:1304.6192

  4. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw 30(1–7):107–117

    Google Scholar 

  5. Caster F, Gilreath L, Pan S, Wang Z, Capolino F, Heydari P (2014) Design and analysis of a w-band 9-element imaging array receiver using spatial-overlapping super-pixels in silicon. J Solid-State Circ 49(6):1317–1332

    Article  Google Scholar 

  6. Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM TIST 2(3):27

    Google Scholar 

  7. Choi J, Croft WB, Kim J (2012) Quality models for microblog retrieval. In: CIKM, pp 1834–1838

  8. Clements PC, Bass L (2010) Using business goals to inform a software architecture. In: RE, pp 69–78

  9. Csaba B, Pluhár A (2007) A randomized algorithm for the on-line weighted bipartite matching problem. CoRR, arXiv:0705.4673

  10. Felzenszwalb PF, Girshick RB, McAllester DA, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans PAMI 32 (9)

  11. Fu Q, Zhang Y, Xu L, Li H (2013) A method of shot-boundary detection based on hsv space. In: CIS, pp 219–223

  12. Gao Y, Gupta SK, Wang Y, Pedram M (2014) An energy-aware fault tolerant scheduling framework for soft error resilient cloud computing systems. In: DATE, pp 1–6

  13. Gu Y, Gao C, Cong G, Yu G (2014) Effective and efficient clustering methods for correlated probabilistic graphs. IEEE Trans Knowl Data Eng 26(5):1117–1130

    Article  Google Scholar 

  14. Harvey M, Crestani F, Carman MJ (2013) Building user profiles from topic models for personalised search. In: CIKM, pp 2309–2314

  15. Hu H, Zhu X, Wang Y, Pan R, Zhu J, Bonomi F (2013) Proxy-based multi-stream scalable video adaptation over wireless networks using subjective quality and rate models. IEEE Trans Multimed 15(7):1638–1652

    Article  Google Scholar 

  16. Huang J, Gutta S, Wechsler H (1996) Detection of human faces using decision trees. In: FG, pp 248–252

  17. Ji Z, Xu F, Wang B, He B (2012) Question-answer topic model for question retrieval in community question answering. In: CIKM, pp 2471–2474

  18. Jiang X, Yao H, Chua T-S, Sun X, Zhao S, Gao Y (2014) Exploring principles-of-art features for image emotion recognition. ACM MM

  19. Li F, He T, Tu X, Hu X (2012) Incorporating word correlation into tag-topic model for semantic knowledge acquisition. In: CIKM, pp 1622–1626

  20. Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition. In: CIKM, pp 733–738

  21. Marín B, Giachetti G, Pastor O, Vos TEJ, Abran A (2013) Using a functional size measurement procedure to evaluate the quality of models in mdd environments. ACM Trans Softw Eng Methodol 22(3):26

    Article  Google Scholar 

  22. Mendelzon AO (2000) Review - authoritative sources in a hyperlinked environment. ACM SIGMOD Digit Rev 1

  23. Pei W-J, Zhang Y-L, Zhang Y, Zheng C-H (2014) Pedestrian detection based on hog and lbp. In: ICIC (1), pp 715–720

  24. Roul RK, Devanand OR, Sahay SK (2014) Web document clustering and ranking using tf-idf based apriori approach. CoRR, arXiv:1406.5617

  25. Sakai T, Dou Z (2013) Summaries, ranked retrieval and sessions: a unified framework for information access evaluation. In: SIGIR, pp 473–482

  26. Severyn A, Moschitti A, Tsagkias M, Berendsen R, de Rijke M (2014) A syntax-aware re-ranker for microblog retrieval. In: SIGIR, pp 1067–1070

  27. Takeuchi Y, Sugimoto M (2006) Cityvoyager: an outdoor recommendation system based on user location history. In: UIC, pp 625–636

  28. Tang X, Yang CC (2012) Tut: a statistical model for detecting trends, topics and user interests in social media. In: CIKM, pp 972–981

  29. Wei X, Croft WB (2006) Lda-based document models for ad-hoc retrieval. In: SIGIR, pp 178–185

  30. Wu Z, Palmer MS (1994) Verb semantics and lexical selection. In: ACL

  31. Yuan Z-W, Zhang H (2012) Research on application of kuhn-munkres algorithm in emergency resources dispatch problem. In: FSKD, pp 2774–2777

  32. Zhang D, Mao R, Li H, Mao J (2011) How to count thumb-ups and thumb-downs?: an information retrieval approach to user-rating based ranking of items. In: SIGIR, pp 1223–1224

  33. Zhang L, Gao Y, Hong C, Feng Y, Zhu J, Cai D (2014) Feature correlation hypergraph: Exploiting high-order potentials for multimodal recognition. IEEE T Cybern 44(8):1408–1419

    Article  Google Scholar 

  34. Zhang L, Gao Y, Xia Y, Dai Q, Li X (2014) A fine-grained image categorization system by cellet-encoded spatial pyramid modeling. IEEE Trans Ind Electron 22(12):5071–5084

    Google Scholar 

  35. Zhang L, Han Y, Yang Y, Song M, Yan S, Tian Q (2013) Discovering discriminative graphlets for aerial image categories recognition. IEEE Trans Image Process 22(12):5071–5084

    Article  MathSciNet  Google Scholar 

  36. Zhang L, Song M, Liu X, Bu J, Chen C (2013) Fast multi-view segment graph kernel for object classification. Signal Process 93(6):1597–1607

    Article  Google Scholar 

  37. Zhang L, Song M, Liu X, Sun L, Chen C, Bu J (2014) Recognizing architecture styles by hierarchical sparse coding of blocklets. Inf Sci 254(8):141–154

    Article  Google Scholar 

  38. Zhang X, Xu Y, Hu H, Liu Y, Guo Z, Wang Y (2013) Modeling and analysis of skype video calls: rate control and video quality. IEEE Trans Multimed 15(6):1446–1457

    Article  Google Scholar 

  39. Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with gps history data. In: WWW, pp 1029–1038

  40. Zhu X, Guo J, Cheng X, Lan Y (2012) More than relevance: high utility query recommendation by mining users’ search behaviors. In: CIKM, pp 1814–1818

Download references

Acknowledgments

I would like to express my deep gratitude to Prof. Chua and the NeXT group in National University of Singapore. This work was supported in part by the National Natural Science Foundation of China (61472275, 61100124, 21106095, 61170239, and 61202168), the grant of Elite Scholar Program of Tianjin University.

Author information

Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, China

    Weizhi Nie, Anan Liu, Xiaorong Zhu & Yuting Su

Authors
  1. Weizhi Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaorong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuting Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anan Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nie, W., Liu, A., Zhu, X. et al. Quality models for venue recommendation in location-based social network. Multimed Tools Appl 75, 12521–12534 (2016). https://doi.org/10.1007/s11042-014-2339-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2339-x

Keywords

Search

Navigation