Accesso libero

Time-Aware and Grey Incidence Theory Based User Interest Modeling for Document Recommendation

INFORMAZIONI SU QUESTO ARTICOLO

Cita

1. Resnick, P., H. R. Varian. Recommender Systems. - Communications of the ACM, Vol. 40, 1997, No 3, pp. 56-58.10.1145/245108.245121Search in Google Scholar

2. Adomavicius, G., A. Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. - IEEE Transactions on Knowledge and Data Engineering, Vol. 17, 2005, No 6, pp. 734-749.10.1109/TKDE.2005.99Search in Google Scholar

3. Zhou, X, Y. Xu, Y. Li et al. The State-of-the-Art in Personalized Recommender Systems for Social Networking. - Artificial Intelligence Review, Vol. 37, 2012, No 2, pp. 119-132.10.1007/s10462-011-9222-1Search in Google Scholar

4. De Campos, L. M., J. M. Fernández-Luna, J. F. Huete et al. Combining Content- Based and Collaborative Recommendations: A Hybrid Approach Based on Bayesian Networks. - International Journal of Approximate Reasoning, Vol. 51, 2010, No 7, pp. 785-799.10.1016/j.ijar.2010.04.001Search in Google Scholar

5. Liu, J., P. Dolan, E. R. Pedersen. Personalized News Recommendation Based on Click Behaviour. - In: Proc. of 15th International Conference on Intelligent User Interfaces (IUI), February 2010, pp. 31-40.10.1145/1719970.1719976Search in Google Scholar

6. Lew, M. S., N. Sebe, C. Djeraba et al. Content-Based Multimedia Information Retrieval: State of the Art and Challenges. - ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), Vol. 2, 2006, No 1, pp. 1-19.10.1145/1126004.1126005Search in Google Scholar

7. Yu, K., A. Schwaighofer, V. Tresp. Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes. - In: Proc. of 19th Conference on Uncertainty in Artificial Intelligence (UAI), August 2002, pp. 616-623.Search in Google Scholar

8. Wang, Z., L. Sun, W. Zhu et al. Joint Social and Content Recommendation for User- Generated Videos in Online Social Network. - IEEE Transactions on Multimedia, Vol. 15, 2013, No 3, pp. 698-709.10.1109/TMM.2012.2237022Search in Google Scholar

9. Gong, S. Learning User Interest Model for Content-Based Filtering in Personalized Recommendation System. - JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, 2012, No 11, pp. 155-162.10.4156/jdcta.vol6.issue11.20Search in Google Scholar

10. Campos, P. G., F. Díez, I. Cantador. Time-Aware Recommender Systems: A Comprehensive Survey and Analysis of Existing Evaluation Protocols. - User Modeling and User-Adapted Interaction, Vol. 24, 2014, No 1-2, pp. 67-119.10.1007/s11257-012-9136-xSearch in Google Scholar

11. Baltrunas, L., X. Amatriain. Towards Time-Dependant Recommendation Based on Implicit Feedback. - In: Proc. of Workshop on Context-Aware Recommender Systems (CARS’09), October 2009.Search in Google Scholar

12. Koren, Y. Collaborative Filtering with Temporal Dynamics. - Communications of the ACM, Vol. 53, 2010, No 4, pp. 89-97.10.1145/1721654.1721677Search in Google Scholar

13. Liu, N. N., M. Zhao, E. Xiang et al. Online Evolutionary Collaborative Filtering. - In: Proc. of 4th ACM Conference on Recommender Systems (RecSys’10), ACM, September 2010, pp. 95-102.10.1145/1864708.1864729Search in Google Scholar

14. Musto, C. Enhanced Vector Space Models for Content-Based Recommender Systems. - In: Proc. of 4th ACM Conference on Recommender Systems (RecSys’10), September 2010, pp. 361-364.10.1145/1864708.1864791Search in Google Scholar

15. Raghavan, V. V., S. K. M. Wong. A Critical Analysis of Vector Space Model for Information Retrieval. - Journal of the American Society for Information Science, Vol. 37, 1986, No 5, pp. 279-287.10.1002/(SICI)1097-4571(198609)37:5<279::AID-ASI1>3.0.CO;2-QSearch in Google Scholar

16. Skillen, K. L., L. Chen, C. D. Nugent et al. Ontological User Modelling and Semantic Rule-Based Reasoning for Personalisation of Help-on-Demand Services in Pervasive Environments. - Future Generation Computer Systems, Vol. 34, 2014, pp. 97-109.10.1016/j.future.2013.10.027Search in Google Scholar

17. Sarwar, B., G. Karypis, J. Konstan et al. Item-Based Collaborative Filtering Recommendation Algorithms. - In: Proc. of 10th International Conference on World Wide Web (WWW), April 2001, pp. 285-295.10.1145/371920.372071Search in Google Scholar

18. Zhang, W., T. Yoshida, X. Tang. A Comparative Study of TF*IDF, LSI and Multi-Words for Text Classification. - Expert Systems with Applications, Vol. 38, 2011, No 3, pp. 2758-2765.10.1016/j.eswa.2010.08.066Search in Google Scholar

19. Ding, Y., X. L i. Time Weight Collaborative Filtering. - In: Proc. of 14th ACM International Conference on Information and Knowledge Management (CIKM), ACM, October 2005, pp. 485-492.10.1145/1099554.1099689Search in Google Scholar

20. Liu, S. F., Z. G. Fang, Y. L i n. Study on a New Definition of Degree of Grey Incidence. - Journal of Grey System, Vol. 9, 2006, No 2, pp. 115-122.Search in Google Scholar

21. Liu, S. F., N. M. Xie, J. Forrest. Novel Models of Grey Relational Analysis Based on Visual Angle of Similarity and Nearness. - Grey Systems: Theory and Application, Vol. 1, 2011, No 1, pp. 8-18.10.1108/20439371111106696Search in Google Scholar

22. Deng, J. L. Introduction to Grey System Theory. - The Journal of Grey System, Vol. 1, 1989, No 1, pp. 1-24.Search in Google Scholar

23. Xiao, X., W. Z. Lin. Application of Protein Grey Incidence Degree Measure to Predict Protein Quaternary Structural Types. - Amino Acids, Vol. 37, 2009, No 4, pp. 741-749.10.1007/s00726-008-0212-9Search in Google Scholar

24. Yaoguo, D., L. Sifeng, L. Bin et al. Improvement on Degree of Grey Slope Incidence. - Engineering Science, Vol. 2004, No 3, pp. 41-44.Search in Google Scholar

25. Guoliang, Z. S. Z. Comparison between Computation Models of Grey Interconnect Degree and Analysis on Their Shortages. - Systems Engineering, Vol. 14, 1996, No 3, pp. 45-49.Search in Google Scholar

26. Hernández del Olmo, F., E. Gaudioso. Evaluation of Recommender Systems: A New Approach. -Expert Systems with Applications, Vol. 35, 2008, No 3, pp. 790-804.10.1016/j.eswa.2007.07.047Search in Google Scholar

27. http://nlp.stanford.edu/software/segmenter.shtml Search in Google Scholar

eISSN:
1314-4081
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, Information Technology