Abstract
In this paper, we address the recommendation process as a one-class classification problem based on content features and a Negative Selection (NS) algorithm that captures user preferences. Specifically, we develop an Artificial Immune System (AIS) based on a Negative Selection Algorithm that forms the core of a music recommendation system. The NS-based learning algorithm allows our system to build a classifier of all music pieces in a database and make personalized recommendations to users. This is achieved quite efficiently through the intrinsic property of NS algorithms to discriminate “self-objects” (i.e. music pieces of user’s like) from “non self-objects”, especially when the class of non self-objects is vast when compared to the class of self-objects and the examples (samples) of music pieces come only from the class of self-objects (music pieces of user’s like). Our recommender has been fully implemented and evaluated and found to outperform state of the art recommender systems based on support vector machines methodologies.
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Lampropoulos, A.S., Sotiropoulos, D.N., Tsihrintzis, G.A. (2010). A Music Recommender Based on Artificial Immune Systems. In: Tsihrintzis, G.A., Damiani, E., Virvou, M., Howlett, R.J., Jain, L.C. (eds) Intelligent Interactive Multimedia Systems and Services. Smart Innovation, Systems and Technologies, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14619-0_17
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DOI: https://doi.org/10.1007/978-3-642-14619-0_17
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