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

移动中的个人，例如，在一个不熟悉的城市观光旅游的游客经常发现自己被应付不熟悉环境的挑战所压倒。这就需要一些工具和方法，在他们“移动”时，通过向他们提供有用的建议来指导他们。“移动的和基于传感器的技术的最新进展使得在许多不同的移动的应用中收集和处理位置轨迹成为可能。这些数据，当与其他时空，上下文和用户特定的信息相结合时，原则上可以用于为移动中的个人生成有用的建议。这个探索性的研究项目制定和探索一个新的变种的推荐系统，即，移动的顺序推荐系统的移动的用户，其中每个建议考虑到过去的建议的轨迹和历史，作为一个选择一个序列的位置推荐下的一组时空，上下文和隐私的限制。 鉴于问题的组合性质（搜索空间的大小在相关参数中呈指数增长），该项目旨在探索数学。该项目还将制定适当的措施来评估替代解决方案的有效性，如果成功，该项目将确定一系列调查的可行性，从而导致制定有效的方法来解决顺序推荐问题，为移动的用户带来明显的好处。该项目丰富了研究生和本科生基于研究的高级培训机会。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界。

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.