Abstract
Since the World Wide Web has become widespread, more and more applications exist that are suitable for the application of social information filtering techniques. In collaborative filtering, preferences of a user are estimated through mining data available about the whole user population, implicitly exploiting analogies between users that show similar characteristics. These preferences are then normally used to filter content or functionality of an application. Two important factors for the quality of the filtering process are the number of users and the amount of information (such as observed behaviors) available about each user. Another factor is the number of objects in the pool of the application that can be considered during the filtering process. Today in most cases memory based approaches to collaborative filtering are used. Unfortunately with O(#users * #items) those do not scale well. Therefore we implemented a model based approach using two different types of neural networks and benchmarked them against a widely used memory based approach. Especially with ART2 networks we obtained some encouraging results.
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Graef, G., Schaefer, C. (2002). Application of ART2 Networks and Self-Organizing Maps to Collaborative Filtering. In: Reich, S., Tzagarakis, M.M., De Bra, P.M.E. (eds) Hypermedia: Openness, Structural Awareness, and Adaptivity. AH 2001. Lecture Notes in Computer Science, vol 2266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45844-1_27
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DOI: https://doi.org/10.1007/3-540-45844-1_27
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