Recommendation system for web article based on association rules and topic modelling

Guntur Budi Herwanto, Annisa Maulida Ningtyas


The World Wide Web is now the primary source for information discovery. A user visits websites that provide information and browse on the particular information in ac-cordance with their topic interest. Through the navigational process, visitors often had to jump over the menu to find the right content. Recommendation system can help the visitors to find the right content immediately. In this study, we propose a two-level recommendation system, based on association rule and topic similarity. We generate association rule by applying Apriori algorithm. The dataset for association rule mining is a session of topics that made by combining the result of sessionization and topic modeling. On the other hand, the topic similarity made by comparing the topic proportion of web article. This topic proportion inferred from the Latent Dirichlet Allocation (LDA). The results show that in our dataset there are not many interesting topic relations in one session. This result can be resolved, by utilizing the second level of recommendation by looking into the article that has the similar topic.


Website; Recommendation; Topic Modelling; Latent Dirichlet Allocation; Association Rule

Full Text:



U. Gretzel, “Intelligent systems in tourism,” Ann. Tour. Res., vol. 38, no. 3, pp. 757–779, Jul. 2011 [Online]. Available:

O. Arbelaitz, I. Gurrutxaga, A. Lojo, J. Muguerza, J. M. Pérez, and I. Perona, “Web usage and content mining to extract knowledge for modelling the users of the Bidasoa Turismo website and to adapt it,” Expert Syst. Appl., vol. 40, no. 18, pp. 7478–7491, Dec. 2013 [Online]. Available:

D. Buhalis and R. Law, “Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research,” Tour. Manag., vol. 29, no. 4, pp. 609–623, Aug. 2008 [Online]. Available:

B. Pan and D. R. Fesenmaier, “Travel information search on the internet: a preliminary analysis.,” 2003, pp. 242–251.

J. Borges and M. Levene, “Data Mining of User Navigation Patterns,” pp. 92–112, 2007.

A. S. Lalani, “Data Mining of Web Access Logs,MS Thesis,” pp. 1–70, 2003.

S. S. Rawat and L. Rajamani, “Discovering Potential User Browsing Behaviors Using Custom-Built Apriori Algorithm,” Int. J. Comput. Sci. Inf. Technol., vol. 2, no. 4, pp. 28–37, 2010.

B. Liu, Web Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011 [Online]. Available:

M. Dimitrijevic and Z. Bosnjak, “Pruning statistically insignificant association rules in the presence of high-confidence rules in web usage data,” Procedia Comput. Sci., vol. 35, no. C, pp. 271–280, 2014 [Online]. Available:

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487–499 [Online]. Available:

E. Lazcorreta, F. Botella, and A. Fernández-Caballero, “Towards Personalized Recommendation by Two-step Modified Apriori Data Mining Algorithm,” Expert Syst. Appl., vol. 35, no. 3, pp. 1422–1429, 2008 [Online]. Available:

D. M. Blei, “,” Cs.Princeton.Edu, pp. 77–84 [Online]. Available:

O. Arbelaitz, I. Gurrutxaga, A. Lojo, J. Muguerza, J. M. Pérez, and I. Perona, “Enhancing a Web Usage Mining based Tourism Website Adaptation with Content Information.”

M. Zanker, M. Fuchs, W. Höpken, M. Tuta, and N. Müller, “Evaluating Recommender Systems in Tourism — A Case Study from Austria,” 2008, pp. 24–34.

J. Vellingiri and S. C. Pandian, “A Survey on Web Usage Mining,” Glob. J. Comput. Sci. Technol., vol. 11, no. 4, pp. 67–72, 2011.

Sunil and M. Doja, Recommender System Based on Web Usage Mining for Personalized E-learning Platforms, vol. 5. 2017.

D. M, A. G. M. Erwin, and N. D, “2013, News Recommendation in Indonesian Language Based on User Click Behaviour.”

N. More and N. P. More, “Recommendation of Books Using Improved Apriori Algorithm,” Int. J. Innov. Res. Sci. Technol, vol. 1, no. 4, pp. 80–82, 2014.

H. . Husin, “News Recommendation Based on Web Usage and Web Content Mining,” in ICDE Workshops, 2013.

N. Dave, K. Potts, V. Dinh, and H. U. Asuncion, “Combining association mining with topic modeling to discover more file relationships,” Int. J. Adv. Softw., vol. 7, no. 3, pp. 3–4, 2014.


Article Metrics

Abstract view : 0 times
PDF - 0 times


  • There are currently no refbacks.

Copyright (c) 2018

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


ISSN : 1978-0524 (print) | 2528-6374 (online)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View JIFO stats