In this study, we propose an advanced mobile application recommendation method that can systematically adjust the level of serendipity. Previous studies focused on generating recommendations that are relevant to users’ interests. However, this leads to a lack of serendipity and novelty for the user and is therefore not sufficient to achieve high levels of user satisfaction. Our model is trained to predict the next application to be recommended based on a disentangled representation of the sequence in which applications were previously used. We introduce variety into our model by varying the attributes of the disentangled representation. The results of our experiments indicate that the proposed method can systematically and effectively adjust the level of serendipity by varying the values and dimensions of the attribute variables in the disentangled representation. Additionally, our method produces recommendations that are superior to those of other benchmark methods in terms of serendipity and relevance.

在这项研究中，我们提出了一种先进的移动应用推荐方法，该方法可以系统地调整偶然性水平。先前的研究集中在生成与用户兴趣相关的建议。但是，这导致用户缺乏偶然性和新颖性，因此不足以实现高水平的用户满意度。我们的模型经过训练，可以根据以前使用过的应用程序的序列的离散表示来预测要推荐的下一个应用程序。我们通过改变解缠结表示的属性将多样性引入模型。我们的实验结果表明，该方法可以通过改变解缠结表示中的属性变量的值和维度来系统有效地调整偶然性水平。此外，在偶然性和相关性方面，我们的方法所提出的建议要优于其他基准方法。