Abstract
Searching in huge amount of information available on the internet is undoubtedly a challenging task. A lot of new web sites are created every day, containing not only text, but other types of resources: e.g. songs, movies or images. As a consequence, a simple search result list from search engines becomes insufficient. Recommender systems are the solution supporting users in finding items, which are interesting for them. These items may be information as well as products, in general. The main distinctive feature of recommender systems is taking into account personal needs and taste of users. Collaborative filtering approach is based on users’ interactions with the electronic system. Its main challenge is generating on-line recommendations in reasonable time coping with large size of data. Appropriate tool to support recommender systems in increasing time efficiency are clustering algorithms, which find similarities in off-line mode. Commonly, it involves decreasing of prediction accuracy of final recommendations. This article presents an approach based on clustered data, which prevents the negative consequences, keeping high time efficiency. An input data are clusters of similar items and searching the items for recommendation is limited to the elements from one cluster.
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Kużelewska, U., Wichowski, K. (2015). A Modified Clustering Algorithm DBSCAN Used in a Collaborative Filtering Recommender System for Music Recommendation. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Theory and Engineering of Complex Systems and Dependability. DepCoS-RELCOMEX 2015. Advances in Intelligent Systems and Computing, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-319-19216-1_23
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DOI: https://doi.org/10.1007/978-3-319-19216-1_23
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