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Heuristic Approach to Online Purchase Prediction Based on Internet Store Visitors Classification Using Data Mining Methods

机译:基于Internet商店的在线购买预测的启发式方法使用数据挖掘方法对访问者进行分类

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Last years research gave some preliminary results in approaches to customer online purchase prediction. However, it still remains unclear what exact set of features of data instances should be incorporated in a model and is enough for prediction, what is the best data mining method (algorithm) to use, how stable over time could be such a model, whether a model is transferable from one online store to another. This study is focused on a heuristic approach to dealing with the problem under conditions of such theoretical and methodological diversity in order to find a quick and inexpensive first approximation to the solution or at least to find useful patterns and facts in the data.
机译:去年,研究对客户在线购买预测的方法进行了一些初步结果。但是,它仍然尚不清楚数据实例的确切特征集应该在模型中结合什么,足以进行预测,使用最佳的数据挖掘方法(算法),随着时间的推移多么稳定,是多么的模型模型可从一个在线商店转移到另一家。本研究专注于在这种理论和方法论多样性的条件下处理问题的启发式方法,以便找到对解决方案的快速且廉价的第一个近似,或者至少在数据中找到有用的模式和事实。

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