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

New York University, New York NY

Investigators

Abstract

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.