Abstract
Nowadays, WWW services compete intensively to attract attention of visitors. They search new solutions to increase attractiveness of the systems and to satisfy all customer expectations. To achieve this they often offer an individual approach to each user, e.g. applying recommender systems. A recommender system is able to learn a customer’s preferences and recommend products, which the user is probably interested in. The recommendations are based on similarity between registered users’ activity, e.g. items, which they visited or bought.
The purpose of this paper is to find a reasonable solution to offer recommendations on internet forum. Since clustering algorithms were useful to group similar posts (according to preliminary results), they were chosen as the tool to generate recommendations. The algorithms available in Apache Mahout were used in the experiments described in this article. Finally, the recommender system has been implemented on the forum, and their effectiveness was examined, as well. The results confirmed the validity of the proposed solution.
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Kużelewska, U., Guziejko, E. (2014). A Recommender System Based on Content Clustering Used to Propose Forum Articles. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 30 – July 4, 2014, Brunów, Poland. Advances in Intelligent Systems and Computing, vol 286. Springer, Cham. https://doi.org/10.1007/978-3-319-07013-1_27
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DOI: https://doi.org/10.1007/978-3-319-07013-1_27
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