Conference Paper

CompRec-Trip: A composite recommendation system for travel planning.

DOI: 10.1109/ICDE.2011.5767954 Conference: Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, April 11-16, 2011, Hannover, Germany
Source: DBLP

ABSTRACT Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or a DVD. However, applications such as travel planning can benefit from a system capable of recommending packages of items, under a user-specified budget and in the form of sets or sequences. In this context, there is a need for a system that can recommend top-k packages for the user to choose from. In this paper, we propose a novel system, CompRec-Trip, which can automatically generate composite rec- ommendations for travel planning. The system leverages rating information from underlying recommender systems, allows flex- ible package configuration and incorporates users' cost budgets on both time and money. Furthermore, the proposed CompRec- Trip system has a rich graphical user interface which allows users to customize the returned composite recommendations and take into account external local information. I. INTRODUCTION

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To enable smart transportation, a large volume of vehicular GPS trajectory data has been collected in the metropolitan-scale Shanghai Grid project. The collected raw GPS data, however, suffers from various errors. Thus, it is inappropriate to use the raw GPS dataset directly for many potential smart transportation applications. Map matching, a process to align the raw GPS data onto the corresponding road network, is a commonly used technique to calibrate the raw GPS data. In practice, however, there is no ground truth data to validate the calibrated GPS data. It is necessary and desirable to have ground truth data to evaluate the effectiveness of various map matching algorithms, especially in complex environments. In this paper, we propose truthFinder, an interactive map matching system for ground truth data exploration. It incorporates traditional map matching algorithms and human intelligence in a unified manner. The accuracy of truthFinder is guaranteed by the observation that a vehicular trajectory can be correctly identified by human-labeling with the help of a period of historical GPS dataset. To the best of our knowledge, truthFinder is the first interactive map matching system trying to explore the ground truth from historical GPS trajectory data. To measure the cost of human interactions, we design a cost model that classifies and quantifies user operations. Having the guaranteed accuracy, truthFinder is evaluated in terms of operation cost. The results show that truthFinder makes the cost of map matching process up to two orders of magnitude less than the pure human-labeling approach.
  • [Show abstract] [Hide abstract]
    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.
    Proceedings of the 7th ACM international conference on Web search and data mining; 02/2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: When planning a trip, one essential task is to find a set of Places-of-Interest (POIs) which can be visited during the trip. Using existing travel guides or websites such as Lonely Planet and TripAdvisor, the user has to either manually work out a desirable set of POIs or take pre-configured travel packages; the former can be time consuming while the latter lacks flexibility. In this demonstration, we propose an Interactive Package configuration System (IPS), which visualizes different candidate packages on a map, and enables users to configure a travel package through simple interactions, i.e., comparing packages and fixing/removing POIs from a package. Compared with existing trip planning systems, we believe IPS strikes the right balance between flexibility and manual effort.
    Proceedings of the VLDB Endowment. 08/2013; 6(12):1362-1365.

Full-text (2 Sources)

Available from
May 16, 2014

Min Xie