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

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Available from: Laks Lakshmanan, Sep 25, 2015
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    • "Smart transportation is expected to play an important role to meet the growing demand of various transportationrelated services from citizens [16] and government officers [2], especially in modern cities. A fundamental requirement to smart transportation is to collect the dynamic vehicular location data to form the basis to build an effective traffic information system [4]. "
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    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.
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    ABSTRACT: Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow, given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend the top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender systems focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of decidingwhether there is a recommendation (package)whose cost is under a given budget and whose value exceeds some threshold is NP-complete, we devise several approximation algorithms for generating the top-k packages as recommendations. We analyze the efficiency as well as approximation quality of these algorithms. Finally, using two real and two synthetic datasets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for the top-k packages compared to exact algorithms.
    Frontiers of Computer Science (print) 06/2012; 6(3). DOI:10.1007/s11704-012-2014-1 · 0.43 Impact Factor
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    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.
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