Related papers: Learning Points and Routes to Recommend Trajectori…

Recently, a new approach to perform regression testing has been defined: the point of interest (POI) testing. A POI, in this context, is any expression of a program. The approach receives as input a set of relations between POIs from a…

This paper presents a test collection for contextual point of interest (POI) recommendation in a narrative-driven scenario. There, user history is not available, instead, user requests are described in natural language. The requests in our…

Recommending Points-of-Interest (POIs) is surfacing in many location-based applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make…

The exponential growth of Location-based Social Networks (LBSNs) has greatly stimulated the demand for precise location-based recommendation services. Next Point-of-Interest (POI) recommendation, which aims to provide personalised POI…

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been…

With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the…

Trip recommender system, which targets at recommending a trip consisting of several ordered Points of Interest (POIs), has long been treated as an important application for many location-based services. Currently, most prior arts generate…

Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route…

Social media platforms enable users to share diverse types of information, including geolocation data that captures their movement patterns. Such geolocation data can be leveraged to reconstruct the trajectory of a user's visited Points of…

Point-of-Interest (POI) recommender systems provide personalized recommendations to users and help businesses attract potential customers. Despite their success, recent studies suggest that highly data-driven recommendations could be…

We introduce and study the problem in which a mobile sensing robot (our tourist) is tasked to travel among and gather intelligence at a set of spatially distributed point-of-interests (POIs). The quality of the information collected at each…

When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness and other features that influence the way they perceive…

We consider a practical top-k route search problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination and limited in a cost…

Point-of-interest (POI) recommendation considers spatio-temporal factors like distance, peak hours, and user check-ins. Given their influence on both consumer experience and POI business, it's crucial to consider fairness from multiple…

Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Such questions express various kinds of spatial and non-spatial constraints, necessitating a combination of textual and spatial…

A well-crafted police patrol route design is vital in providing community safety and security in the society. Previous works have largely focused on predicting crime events with historical crime data. The usage of large-scale mobility data…

Route choice in multimodal networks shows a considerable variation between different individuals as well as the current situational context. Personalization of recommendation algorithms are already common in many areas, e.g., online retail.…

Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit. It is strongly desirable for the recommended sequence to avoid loops, as tourists typically would not wish to revisit the same…

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as…

Human mobility prediction forecasts a user's next Point of Interest (POI) from historical trajectories, supporting applications from recommendation to urban planning. Recent studies have recognized the problem with long-tail POIs in human…