Indonesian News Harvester and Recommender System

Adi Wibowo, Rolly Intan, Nydia Valentina

Abstract


To provide convenience for the user that frequently read the news, a system to gather, classify, and provide news from several news websites in one place was needed. This system utilized a recommender system to provide only relevant news to the user. This research proposed a system architecture that used vector space model, and Rocchio relevance feedback to provide specific news recommendation to user’s feedback. The results are that the proposed system architecture can achieve the goal by using five levels of feedback from the user. However, the time needed to gather news is increasing exponentially in line with the number of terms gathered from articles.


Keywords


recommender system, vector space model, Rocchio relevance feedback

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References


Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132. https://doi.org/10.1016/j.knosys.2013.03.012

Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. https://doi.org/10.1023/A:1021240730564

Hameed, M. A., Jadaan, O. A., & Ramachandram, S. (2012). Collaborative filtering based recommendation system: A survey. International Journal on Computer Science and Engineering, 4(5), 859-876.

Karaa, W. B. A. (2013). A new stemmer to improve information retrieval. International Journal of Network Security & Its Applications, 5(4), 143-154. https://doi.org/10.5121/ijnsa.2013.5411

Palanivel, K., & Sivakumar, R. (2010). A study on implicit feedback in multicriteria e-commerce recommender system. Journal of Electronic Commerce Research, 11(2), 140-156.

Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web (Vol. 4321, pp. 325-341). Springer Berlin Heidelberg.

Rocchio, J. J. (1971). Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing (pp. 313-323). Englewood Cliffs, NJ: Prentice Hall Inc.

Ropero, J., Gómez, A., Carrasco, A., León, C., & Luque, J. (2012). Term weighting for information retrieval using fuzzy logic. In E. P. Dadios (Ed.), Fuzzy logic - algorithms, techniques and implementations (pp.173-192). InTech.

Safoury, L., & Salah, A. (2013). Exploiting user demographic attributes for solving cold-start problem in recommender system. Lecture Notes on Software Engineering, 1(3), 303-307. https://doi.org/10.7763/LNSE.2013.V1.66

Said, A., Plumbaum, T., De Luca, E. W., & Albayrak, S. (2011). A comparison of how demographic data affects recommendation. In 19th International Conference on User Modeling, Adaptation and Personalization (UMAP).

Singh, A. K. (2012). Ajax asynchronous database refresh. International Journal of Information and Communication Technology Research, 2(8), 669-703.

Turney, P. D., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37, 141-188.

Zaman, A. N. K., Matsakis, P., & Brown, C. (2011). Evaluation of stop word lists in text retrieval using latent semantic indexing. In The Sixth IEEE International Conference on Digital Information Management. Melbourne, Australia.




DOI: https://doi.org/10.21512/comtech.v8i3.3912

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