Abstract
Context-Aware Recommender System is known to not only recommend items or services similar to those already rated with the highest score, but also consider the current contexts for personalized Web services recommendation. Specifically, a key step for CARS methods refers to previous service invocation experiences under the similar context of the user to make Quality of Services prediction. Existing works either considered the influence of regional correlations on user preference, or combined the location-aware context with the matrix factorization method. However, the user preference expansion triggered by instant update of user location is not fully observed. For instance, when making Web service recommendation for a user, it is expected to be aware of rapid change of the user location immediately and the expansion of user preference as well. In this paper, we propose a Web services recommendation approach dubbed as CASR-UPE (Context-aware Web Services Recommendation based on User Preference Expansion). First, we model the influence of user location update on user preference. Second, we perform the context-aware similarity mining for updated location. Third, we predict the Quality of Services by Bayesian inference, and thus recommend the best Web service for the user subsequently. Finally, we evaluate the CASR-UPE method on WS-Dream dataset by evaluation matrices such as RMSE and MAE. Experimental results show that our approach outperforms several benchmark methods with a significant margin.
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
Notes
- 1.
Weather China, http://en.weather.com.cn/.
- 2.
Moji Weather China, http://www.moweather.com/.
- 3.
US national weather service, http://www.weather.gov/.
- 4.
Le Figaro météo weather forecasting service, http://www.lefigaro.fr/meteo/france/index.php.
- 5.
WS-Dream dataset, http://www.wsdream.net/dataset.html.
References
Truong, H., Dustdar, S.: A survey on context-aware web service systems. Int. J. Web Inf. Syst. 5(1), 5–31 (2009)
Dourish, P.: What we talk about when we talk about context. Pers. Ubiquit. Comput. 8(1), 19–30 (2003)
Jannach, D., Dortmund, T.U., Friedrich, G.: Tutorial: recommender systems. In: International Joint Conference on Artificial Intelligence, Beijing (2013)
Staiano, J., Oliver, N., Lepri, B., de Oliveira, R., Caraviello, M.: Money walks: a human-centric study on the economics of personal mobile data. In: 2014 ACM Conference on Ubiquitous Computing, pp. 583–594. ACM, Seattle (2014)
Rossi, L., Musolesi, M.: It’s the way you check-in: identifying users in location-based social network. In: 2nd ACM Conference on Online Social Networks, pp. 215–226. ACM, Dublin (2014)
Lima, A., Musolesi, M.: Spatial dissemination metrics for location-based social networks. In: 2012 ACM Conference on Ubiquitous Computing, pp. 972–979. ACM, Pittsburgh (2012)
Papapetrou, P., Roussos, G.: Social context discovery from temporal app use patterns. In: 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 397–402. ACM, Seattle (2014)
Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: 33rd international ACM SIGIR Conference on Research and development in information retrieval, pp. 210–217. ACM, Geneva (2010)
Xiong, H., Zhang, D., Gauthier, V.: Predicting mobile phone user locations by exploiting collective behavioral patterns. In: IEEE 9th International Conference on Ubiquitous Intelligence and Computing (UIC 2012), pp. 164–171. IEEE Press (2012)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, New York (2011)
Guo, B., Chen, C., Zhang, D., Yu, Z., Chin, A.: Mobile crowd sensing and computing: when participatory sensing meets participatory social media. IEEE Commun. Mag. (2015)
Yu, Z., Feng, Y., Xu, H., Zhou, X.: Recommending travel packages based on mobile crowdsourced data. IEEE Commun. Mag. 52(8), 56–62 (2014)
Yin, H., Cui, B., Chen, L.: Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discov. Data 38 (2014)
Yang, D., Zhang, D., Yu, Z.: Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. In: 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 479–488. ACM, Zurich (2013)
Yu, Q., Zheng, Z., Wang, H.: Trace norm regularized matrix factorization for service recommendation. In: 2013 IEEE International Conference on Web Services, pp. 34–41. IEEE press, Santa Clara Marriott (2013)
Cao, B., Liu, J., Tang, M., Zheng, Z., Wang, G.: Mashup service recommendation based on user interest and social network. In: IEEE International Conference on Web Services, pp. 99–106. IEEE Press, Santa Clara Marriott (2013)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2010)
McCarthy, J.: Notes on formalizing context. In: International Joint Conference on Artificial Intelligence, pp. 555–560 (1993)
Brézillon, P.: Task-realization models in contextual graphs. In: Dey, A.K., Kokinov, B., Leake, D.B., Turner, R. (eds.) CONTEXT 2005. LNCS (LNAI), vol. 3554, pp. 55–68. Springer, Heidelberg (2005)
Gao, H., Tang, J., Hu, X., Liu, H.: Modeling temporal effects of human mobile behavior on location-based social networks. In: ACM International Conference on Information and Knowledge Management, pp. 1673–1678. ACM, Maui (2013)
Hu, B., Jamali, M., Ester, M.: Spatio-temporal topic modeling in mobile social media for location recommendation. In: IEEE International Conference on Data Mining, pp. 1073–1078. IEEE press (2013)
Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms For Collaborative Filtering. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: An open architecture for collaborative filtering of netnews. In: ACM Conference on Computer Supported Cooperative Work, pp. 175–186 (1994)
Cui, X., Yin, G., Han, Q., Dong, Y.: An improved time-effectiveness reliability prediction approach of web service. J. Comput. Inf. Syst. 10(4), 1365–1374 (2014)
Fan, X., Hu, Y., Zhang, R.: Context-aware web services recommendation based on user preference. In: IEEE Asia-Pacific Services Computing Conference, pp. 55–61. IEEE press, Fuzhou (2014)
Acknowledgment
This work is supported by the grants from Natural Science Foundation of China (No. 61300232); Ministry of Education of China “Chunhui Plan” Cooperation and Research Project (No. Z2012114, Z2014141); Funds of State Key Laboratory for Novel Software Technology, Nanjing University (KFKT2014B09); Fundamental Research Funds for the Central Universities (lzujbky-2015-100); and China Telecom Corp. Gansu Branch Cuiying Funds (lzudxcy-2014-6).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hu, Y., Fan, X., Zhang, R., Chen, W. (2015). Context-Aware Web Services Recommendation Based on User Preference Expansion. In: Yao, L., Xie, X., Zhang, Q., Yang, L., Zomaya, A., Jin, H. (eds) Advances in Services Computing. APSCC 2015. Lecture Notes in Computer Science(), vol 9464. Springer, Cham. https://doi.org/10.1007/978-3-319-26979-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-26979-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26978-8
Online ISBN: 978-3-319-26979-5
eBook Packages: Computer ScienceComputer Science (R0)