Recently, more and more mobile apps are employed in the marketing field with technical advances. Mobile marketing apps have become a prevalent way for enterprise marketing. Therefore, it has been an important and urgent problem to provide personalized and accurate recommendation in mobile marketing, with a large number of items and limited capability of mobile devices. Recommendation have been investigated widely, however, most existing approaches fail to consider the stability or change of users’ behaviors over time. In this paper, we first propose to mine the periodic trends of users’ consuming behavior from historical records by KNN(K-nearest neighbor) and SVR (support vector regression) based time series prediction, and predict the next time when a user re-purchases the item, so that we can recommend the items which users have purchased before at proper time. Second, we aim to find the regularity of users’ purchasing behavior during different life stages and recommend the new items that are needed and proper for their current life stage. In order to solve this, we mine the mapping model from items to user’s life stage first. Based on the model, users’ current life stage can be estimated from their recent behaviors. Finally, users will be recommended with new items which are proper to their estimated life stage. Experimental results show that it has improved the effectiveness of recommendation obviously by mining users’ consuming behaviors with temporal evolution.

中文翻译：

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利用时间演化挖掘消费行为，以在移动营销应用中进行个性化推荐

近年来，随着技术的进步，越来越多的移动应用程序被用于市场营销领域。移动营销应用程序已成为企业营销的一种普遍方式。因此，在移动营销中提供个性化且准确的推荐，移动商品数量众多且功能有限的问题已经成为重要而紧迫的问题。建议已被广泛研究，但是，大多数现有方法都没有考虑用户行为随时间的稳定性或变化。在本文中，我们首先建议通过基于KNN（K近邻）和SVR（支持向量回归）的时间序列预测从历史记录中挖掘用户的消费行为的周期性趋势，并预测用户下次重新消费时的行为。购买物品，这样我们就可以推荐用户在适当的时候购买过的商品。其次，我们旨在找出用户在不同生命阶段的购买行为的规律性，并推荐适合其当前生命阶段的新物品。为了解决这个问题，我们首先挖掘了从项目到用户生命阶段的映射模型。基于该模型，可以根据用户的近期行为来估计其当前的生活阶段。最后，将向用户推荐适合其估计寿命的新物品。实验结果表明，通过挖掘用户随时间演变的消费行为，明显提高了推荐的有效性。我们旨在找出用户在不同生命阶段的购买行为的规律性，并推荐适合其当前生命阶段的新商品。为了解决这个问题，我们首先挖掘了从项目到用户生命阶段的映射模型。基于该模型，可以根据用户的近期行为来估计其当前生活阶段。最后，将向用户推荐适合其估计寿命的新物品。实验结果表明，通过挖掘用户随时间演变的消费行为，明显提高了推荐的有效性。我们旨在找出用户在不同生命阶段的购买行为的规律性，并推荐适合其当前生命阶段的新商品。为了解决这个问题，我们首先挖掘了从项目到用户生命阶段的映射模型。基于该模型，可以根据用户的近期行为来估计其当前生活阶段。最后，将向用户推荐适合其估计寿命的新物品。实验结果表明，通过挖掘用户随时间演变的消费行为，明显提高了推荐的有效性。将向用户推荐适合其估计寿命的新物品。实验结果表明，通过挖掘用户随时间演变的消费行为，明显提高了推荐的有效性。将向用户推荐适合其估计寿命的新物品。实验结果表明，通过挖掘用户随时间演变的消费行为，明显提高了推荐的有效性。