EAGER: Collaborative Research: Sequential Recommender Systems in Mobile and Pervasive Environments

在移动中的个人，例如，在一个陌生的城市观光旅行的游客，经常发现自己被应对陌生环境的挑战所淹没。这就提出了对工具和方法的需求，这些工具和方法可以在他们“移动”的时候，通过向他们提供有用的建议来指导他们。移动和基于传感器的技术的最新进展使得在许多不同的移动应用程序中收集和处理位置跟踪成为可能。原则上，这些数据与其他时空、上下文和用户特定信息相结合，可用于为移动中的个人生成有用的建议。本探索性研究项目制定并探索了一种新的推荐系统变体，即针对移动用户的移动顺序推荐系统，其中每次推荐都会考虑到过去推荐的轨迹和历史，作为在一组时空、上下文和隐私约束下选择一系列地点进行推荐的系统之一。考虑到问题的组合性质（搜索空间的大小在相关参数中呈指数增长），该项目旨在探索启发式。它还将制定适当措施，以评估各种替代解决办法的有效性。该项目如果成功，将建立一系列研究的可行性，从而开发出有效的方法来解决顺序推荐问题，并为移动用户带来明显的好处。该项目丰富了研究生和本科生以研究为基础的高级培训机会。该项目产生的所有数据、软件和出版物将免费提供给更广泛的研究社区。

Individuals on the move, e.g., tourists on a sightseeing trip in an unfamiliar city often find themselves overwhelmed by the challenges of coping with unfamiliar environments. This presents a need for tools and methods that will guide them by providing them useful recommendations while they are "on the move." Recent advances in mobile and sensor-based technologies have made it possible to collect and process location traces across many different mobile applications. Such data, when combined with other spatio-temporal, contextual, and user-specific information can, in principle, be used to generate useful recommendations for individuals on the move. This exploratory research project formulates and explores a novel variant of recommender systems, namely, mobile sequential recommender systems for mobile users where each recommendation takes into account the trajectory and history of past recommendations, as one of selecting a sequence of locations to recommend under a set of spatio-temporal, contextual, and privacy constraints. Given the combinatorial nature of the problem (where the size of the search space grows is exponential in the relevant parameters) the project aims to explore heuristics. It will also develop appropriate measures for assessing the effectiveness of alternative solutions.The project, if successful, would establish the feasibility of a line of investigation that could lead to the development of effective approaches to sequential recommendation problem with obvious benefits to mobile users. The project enriches research based advanced training opportunities for graduate and undergraduate students. All of the data, software, and publications resulting from the project will be made freely available to the broader research community.