Most of existing e-commerce recommender systems have been designed to recommend the right products to users, based on the history of previous users’ individual transaction records. The real application scenarios of recommendation also have different requirements. From the customer point of view, many users visit the websites anonymously, so a practical way to provide anonymous recommendation is needed. From the marketing point of view, the recommendation list is not only a place to display the correlation of products, but also a place to display the variety of products as well as a tool to promote products. From the data point of view, concentration bias may be a serious problem. In this paper we propose trigger and triggered (TT) model to address all of these issues. First, the proposed model generates trigger and triggered pairs with significant correlations which can be used either to create a practical anonymous recommendation or as an input for products lifecycle modeling. The generated pairs not only reflect the relationships between products but also solve the problem of concentration bias very well. Besides, exposure of products required by marketing can be accomplished in the modeling. Second, by using the pairwise knowledge from the first step, the proposed model can recommend the right product at the right time to stimulate future consumptions and increase customers’ engagement for the off-site case. A real-life retail store data is used to evaluate the proposed model, and the experimental results show that the model can decrease the problem of concentration bias while improving the correlation between recommendation items. The TT model significantly improves the sequential purchases on triggered items.

中文翻译：

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营销系统推荐系统

现有的大多数电子商务推荐系统都是根据先前用户的单个交易记录的历史向用户推荐合适的产品而设计的。推荐的实际应用场景也有不同的要求。从客户的角度来看，许多用户都是匿名访问网站的，因此需要一种实用的方法来提供匿名推荐。从营销的角度来看，推荐列表不仅是展示产品相关性的地方，而且还是展示产品多样性的地方，也是推广产品的工具。从数据的角度来看，浓度偏差可能是一个严重的问题。在本文中，我们提出了触发和触发（TT）模型来解决所有这些问题。第一，提议的模型生成具有显着相关性的触发器和触发器对，可用于创建实用的匿名推荐或用作产品生命周期建模的输入。生成的对不仅反映了产物之间的关系，而且很好地解决了浓度偏差的问题。此外，可以在建模中完成营销所需产品的展示。其次，通过使用第一步中的成对知识，所提出的模型可以在正确的时间推荐正确的产品，以刺激未来的消费并提高客户对异地情况的参与度。实际零售商店数据用于评估建议的模型，实验结果表明，该模型可以减少浓度偏倚的问题，同时改善推荐项之间的相关性。TT模型大大改善了已触发项目的顺序购买。