Recommender systems are software applications that make tailored item suggestions to users, usually with the goal of helping them overcome information overload or make informed choices. This tailoring process is typically based on the assumption that long-term preference information about the individual users is available to the system, most commonly in the form of a user-item rating matrix. In such a setting, the recommendation problem can be abstracted as a “matrix filling” task (Resnick et al. 1994), and often also the information about the time when the preferences were collected and when the recommendations should be delivered are not considered. In many real-world recommendation scenarios, however, these assumptions might not hold and therefore represent an abstraction of a limited suitability. On the one hand, there can be a substantial number of first-time visitors or anonymous users requesting a recommendation. Clearly, no long-term preference information is available for such users. In this case, providing a tailored recommendation can only be done based on the interactions observed in the ongoing session. On the other hand, when long-term user preference information is available, the temporal dimension of this information may play a role. More recent interactions, for example, might be more relevant than the older ones. In addition, there might also be some ordering constraints among recommendations, e.g., not recommending a mobile phone

中文翻译：

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基于会话和顺序推荐的研究方向

推荐系统是为用户提供量身定制的项目建议的软件应用程序，通常旨在帮助他们克服信息过载或做出明智的选择。这种定制过程通常基于这样一个假设，即系统可以获得关于单个用户的长期偏好信息，最常见的是用户-项目评级矩阵的形式。在这种情况下，推荐问题可以抽象为“矩阵填充”任务（Resnick et al. 1994），并且通常不考虑关于何时收集偏好以及何时应该提供推荐的信息。然而，在许多现实世界的推荐场景中，这些假设可能不成立，因此代表了有限适用性的抽象。一方面，可能会有大量的首次访问者或匿名用户请求推荐。显然，此类用户没有长期偏好信息可用。在这种情况下，只能根据在进行中的会话中观察到的交互来提供定制的推荐。另一方面，当长期用户偏好信息可用时，该信息的时间维度可能会起作用。例如，最近的互动可能比旧的互动更相关。此外，推荐之间也可能存在一些排序约束，例如，不推荐手机 只能根据在进行中的会话中观察到的交互来提供量身定制的建议。另一方面，当长期用户偏好信息可用时，该信息的时间维度可能会起作用。例如，最近的互动可能比旧的互动更相关。此外，推荐之间也可能存在一些排序约束，例如，不推荐手机 只能根据在进行中的会话中观察到的交互来提供量身定制的建议。另一方面，当长期用户偏好信息可用时，该信息的时间维度可能会起作用。例如，最近的互动可能比旧的互动更相关。此外，推荐之间也可能存在一些排序约束，例如，不推荐手机