Abstract
Newly emerging location-based online social services, such as Meetup and Douban, have experienced increased popularity and rapid growth. The classical Matrix Factorization methods usually only consider the user-item matrix. Recently, Researchers have extended the matrix adding location context as a tensor and used the Tensor Factorization methods for this scenario. However, in real scenario, the users and events are changing over time, the classical Tensor Factorization methods suffers the limitation that it can only be applied for static settings. In this paper, we propose a general Incremental Tensor Factorization model, which models the appearance changes of a tensor by adaptively updating its previous factorized components rather than recomputing them on the whole data every time the data changed. Experiments show that the proposed methods can offer more effective recommendations than baselines, and significantly improve the efficiency of providing location recommendations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bao, J., Zheng, Y., Mokbel, M.F.: 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, SIGSPATIAL 2012, pp. 199–208. ACM, New York (2012)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 39–46. ACM, New York (2010)
Harshman, R.: Foundations of the Parafac Procedure: Models and Conditions for an “explanatory” Multimodal Factor Analysis. In: UCLC Working Papers in Phonetics, University of California at Los Angeles (1970)
Hu, X., Meng, X., Wang, L.: Svd-based group recommendation approaches: An experimental study of moviepilot. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, CAMRa 2011, pp. 23–28. ACM, New York (2011)
Carroll, J.D., Chang, J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition. Psychometrika 35, 35:283–35:319 (1970)
Vandewalle, J., Lathauwer, L.D., Moor, B.D.: A multilinear singualr value decomposition. SIAM Journal of Matrix Analysis and Applications 21, 1253–1278 (2000)
Li, X., Hu, W., Zhang, Z., Zhang, X., Luo, G.: Robust visual tracking based on incremental tensor subspace learning. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8 (October 2007)
Liu, X., He, Q., Tian, Y., Lee, W.-C., McPherson, J., Han, J.: Event-based social networks: Linking the online and offline social worlds. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 1032–1040. ACM, New York (2012)
O’Brien, G.W.: Information management tools for updating an svd-encoded indexing scheme (1994)
Scellato, S., Noulas, A., Lambiotte, R., Mascolo, C.: Socio-spatial properties of online location-based social networks. In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) ICWSM. The AAAI Press (2011)
Sun, J., Zeng, H., Liu, H., Lu, Y., Chen, Z.: Cubesvd: A novel approach to personalized web search. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 382–390. ACM, New York (2005)
Tucker, L.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279–311 (1966)
Vozalis, M.G., Margaritis, K.G.: Applying svd on item-based filtering. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, ISDA 2005, pp. 464–469. IEEE Computer Society, Washington, DC (2005)
Zhang, W., Wang, J., Feng, W.: Combining latent factor model with location features for event-based group recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 910–918. ACM (2013)
Zheng, N., Li, Q., Liao, S., Zhang, L.: Flickr group recommendation based on tensor decomposition. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 737–738. ACM, New York (2010)
Zheng, Y.: Location-based social networks: Users. In: Zheng, Y., Zhou, X. (eds.) Computing with Spatial Trajectories, pp. 243–276. Springer, New York (2011)
Zheng, Y., Xie, X., Ma, W.-Y.: Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zou, B., Li, C., Tan, L., Chen, H. (2014). Location-Based Recommendation Using Incremental Tensor Factorization Model. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-14717-8_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14716-1
Online ISBN: 978-3-319-14717-8
eBook Packages: Computer ScienceComputer Science (R0)