Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multitaxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes timevarying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In tracedriven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.

Improving BanditBased Recommendations with Spatial Context Reasoning: An Online Evaluation The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a citywide free public WiFi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by banditbased recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping WiFi access points according to their usersu0027 visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves banditbased recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geocontext, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the banditbased recommendation system, both in terms of accuracy and user participation.