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An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal

机译：一种基于自适应共振理论的方法，用于分析公民门户网站中推荐系统的可行性

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摘要

This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal Infoville XXI (http://www.infoville.es). The results favour ART2 algorithms for cluster-based collaborative filtering on this Web portal. Finally, a recommender based on ART2 is developed. The follow-up of real recommendations will allow to improve recommendations by including new behaviours that are observed when users interact with the recommender system.
机译：本文提出了一种方法，可以优化公民Web门户中协作推荐程序的未来准确性。有四个阶段，即用户建模，聚类算法基准测试，预测分析和推荐。第一步是开发具有Web用户数据共同特征的分析模型。这些人工数据集然后用于评估聚类算法的性能，尤其是使用K均值聚类对ART2神经网络进行基准测试。此后，将评估应用于从公民Web门户Infoville XXI（http://www.infoville.es）的访问日志得出的真实数据集上的群集的预测准确性。结果支持该门户网站上基于集群的协同过滤的ART2算法。最后，开发了基于ART2的推荐器。真实推荐的后续操作将通过包括当用户与推荐系统交互时观察到的新行为来改进推荐。

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