Indonesian News Harvester and Recommender System
DOI:
https://doi.org/10.21512/comtech.v8i3.3912Keywords:
recommender system, vector space model, Rocchio relevance feedbackAbstract
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.
Plum Analytics
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.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: