本文认为，用户的相似度除了用user-based CF来表达以为，还可以用app usage-context来表达，也就是app使用的先后次序（或者短时间内使用app 包）
所以本文基于NN技术，提出了两个loss-function，并且share 模型参数。
第一个loss function，是基于常规的用户的行为数据，e.g., 访问过的item为1， 其他为0
第二个loss function，用来预测preference为1的item的usage-context，也就是与它处于一个包的其他app
这两个loss function通过简单的加权融合到一起。作者认为第二个loss function是对第一loss function的约束，从而提高了它的performance。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Leveraging app usage contexts for app recommendation: a neural approach | |||||||||||
| 文献作者 | Yanan Xu | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | |||||||||||
| The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods. | |||||||||||
