Customized tour recommendations in urban areas

Abstract

The ever-increasing urbanization coupled with the unprecedented capacity to collect and process large amounts of data have helped to create the vision of intelligent urban environments. One key aspect of such environments is that they allow people to effectively navigate through their city. While GPS technology and route-planning services have undoubtedly helped towards this direction, there is room for improvement in intelligent urban navigation. This vision can be fostered by the proliferation of location-based social networks, such as Foursquare or Path, which record the physical presence of users in different venues through check-ins. This information can then be used to enhance intelligent urban navigation, by generating customized path recommendations for users.In this paper, we focus on the problem of recommending customized tours in urban settings. These tours are generated so that they consider (a) the different types of venues that the user wants to visit, as well as the order in which the user wants to visit them, (b) limitations on the time to be spent or distance to be covered, and (c) the merit of visiting the included venues. We capture these requirements in a generic definition that we refer to as the TourRec problem. We then introduce two instances of the TourRec problem, study their complexity, and propose efficient algorithmic solutions. Our experiments on real data collected from Foursquare demonstrate the efficacy of our algorithms and the practical utility of the reported recommendations.

ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

“…For example, [12,27,28] use genetic heuristics. Reference [29] utilizes simulated annealing and [13] solves a dynamic programming problem. Reference [14] uses a clustering algorithm based on location and tourist preferences and then computes the route using a greedy algorithm on those clusters.…”

Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

“…The recent work [9] formulated the trip recommendation as a constrained objective function and presented a dynamic programming solution. Their assumption is that POIs can be grouped into several types or categories and the user knows the order of visiting POI types and likes to visit POIs of each type exactly once in a predetermined order.…”

“…Dynamic programming (denoted DP): This is the dynamic programming approach proposed in [9]. We adapt to the order constraint by setting a "global" type to each POI and fix the visiting order that is from "global" type to "global" type.…”

“…We adapt to the order constraint by setting a "global" type to each POI and fix the visiting order that is from "global" type to "global" type. However, the dynamic programming by filling up a 2-dimensional array [9] still cannot deal with the POI availability constraint.…”

“…None of these works focus on the objective to maximize user's happiness. [7,9,18] maximized user's happiness in travel recommendation. [9] assumes a fixed order on the types of POIs visited, which is not suitable in the presence of constraints such as opening hours of POIs.…”