A method for recommending the latest news articles via MinHash and LSH

ABSTRACT

Since most users are more interested in the latest news articles that are recently updated, it is important to recommend those news articles to appropriate users. However, existing methods cannot recommend the latest news articles in a short time. This paper proposes a novel recommendation method focusing on the latest news articles. It spends much shorter execution time than the existing methods thanks to employing two approximation methods, MinHash and locality sensitive hashing. For evaluation, we conducted extensive experiments using a real-world dataset. The experimental results show that our method provides better accuracy and performs much faster than the existing methods.

References

1. Boczkowski, P. 1999. Understanding the Development of Online Newspapers Using Computer-Mediated Communication Theorizing to Study Internet Publishing. New Media & Society. 1, 1, 101--126.
2. Das, A. et al. 2007. Google News Personalization Scalable Online Collaborative Filtering. In Proc. of the Int'l. Conf. on World Wide Web, WWW. 271--280.
3. Pavlik, J. 2000. The Impact of Technology on Journalism. Journalism Studies. 1, 2. 229--237.
4. Li, L. et al. 2011. SCENE: a Scalable Two-stage Personalized News Recommendation System. In Proc. of the Int'l. Conf. on Special Interest Group on Information Retrieval, SIGIR. 125--134.
5. Li, L. et al. 2011. Personalized News Recommendation: A Review and an Experimental Investigation. Computer Science and Technology. 26, 5. 754--766.
6. Lin, C. et al. 2014. Personalized news recommendation via implicit social experts. Information Sciences. 254. 1--18.
7. Adomavicius, G. and Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extensions. IEEE Trans. on Knowledge and Data Engineering, IEEE TKDE. 17, 6. 734--749.
8. Cantador, I., Bellogín, A., and Castells, P. 2008. Ontology-based personalised and context-aware recommendations of news items. In Proc. of the Int'l. Conf. on Web Intelligence and Intelligent Agent Technology, WIIAT. 562--565.
9. Kim, S. et al. 2010. A semantic web-based approach for personalizing news. In Proc. of the Int'l. Conf. on Symposium on Applied Computing, SAC. 854--861.
10. Billsus, D. and Pazzani, M. J. 2000. User modeling for adaptive news access. User modeling and user-adapted interaction. 10, 2--3. 147--180.
11. Hofmann, T. 1999. Probabilistic Latent Semantic Indexing. In Proc. of the Int'l. Conf. on Special Interest Group on Information Retrieval, SIGIR. 50--57.
12. Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent Dirichlet Allocation. Machine Learning Research. 3. 993--1022.
13. Broder, A. Z. 1997. On the Resemblance and Containment of Documents. In Proc. of the Int'l. Conf. on Compression and Complexity of Sequences, CCS. 21--29.
14. Liu, J., Dolan, P. and Pedersen, E. R. 2010. Personalized News Recommendation Based on Click Behavior. In Proc. of the Int'l. Conf. on Intelligent User Interfaces, IUI. 31--40.
15. Claypool, M. et al. 1999. Combining content-based and collaborative filters in an online newspaper. In Proc. of the SIGIR workshop on recommender systems.
16. Chu, W. and Park, S. 2009. Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models. In Proc. of the Int'l. Conf. on World Wide Web, WWW. 691--700.
17. Li, L. et al. 2010. A Contextual-Bandit Approach to Personalized News Article Recommendation. In Proc. of the Int'l. Conf. on World Wide Web, WWW. 661--670.
18. Wu, Y. et al. 2010. On-line Hot Topic Recommendation Using Tolerance Rough Set Based Topic Clustering. Computers. 5, 4. 549--556.
19. Phelan, O., McCarthy, K., and Smyth, B. 2009. Using Twitter to Recommend Real-Time Topical News. In Proc. of the Int'l. Conf. on Recommender Systems, RecSys. 385--388.
20. Lv, Y. et al. 2011. Learning to Model Relatedness for News Recommendation. In Proc. of the Int'l. Conf. on World Wide Web, WWW. 57--66.
21. Shahaf, D. and Guestrin, C. 2010. Connecting the Dots Between News Articles. In Proc. of the Int'l. Conf. on Knowledge Discovery and Data mining, KDD. 623--632.
22. Agarwal, D., Chen B., and Wang, X. 2012. Multi-Faceted Ranking of News Articles using Post-Read Actions. In Proc. of the Int'l. Conf. on Information and Knowledge Management, CIKM. 694--703.
23. Buhler, J. 2001. Efficient Large-Scale Sequence Comparison by Locality-Sensitive Hashing. Bioinformatics. 17, 5. 419--428.

Index Terms

• Information systems

  • Information systems applications

    • Data mining