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A web-based pervasive recommendation system for mobile tourist guides

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Abstract

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.

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Notes

  1. Collaborative filtering, also known as social filtering [46], focuses on the behaviour of users towards items/services, such as purchasing habits or preferences, rather than on the nature of items or services the system offers. In systems that use collaborative filtering approaches, recommendations are made by matching a user to other users that have similar interests and preferences. In this way, each user is suggested items/services that other users with similar interests have chosen in the past. Collaborative filtering techniques work best when there exists a broad user community and each user has already rated a significant number of items [14].

  2. Stereotypes represent an important subject of recommendation systems research which refers to cases where a user that has not yet interacted with the system requires content recommendations. Even more so, in the case that only few users have interacted with the content system to implicitly provide usage data. As such, the system does not hold the required critical data mass to trigger the recommender mechanism and ensure reliable and suitable recommendations. User stereotypes address this problem associating user attributes (age, educational level, gender, profession, etc.) with relative preferences; users characterized by certain attribute values are assumed to have specific preferences, hence, they are recommended relevant content.

  3. Java Specification Requests (JSRs) are formal documents that describe proposed specifications and technologies for adding to the Java platform. A final JSR provides a reference implementation which is a free implementation of the technology in source code form.

  4. MSA is an emerging industry standard that aims to reduce fragmentation and provide a consistent Java ME platform for developers to target. In addition to specifying what component JSRs must be present on a compliant device, the MSA also clarifies behavioural requirements in order to improve the predictability and interoperability of the JSRs. The MSA defines two stacks: a full stack that comprises 16 JSRs (JSR 249), and a subset of eight JSRs (JSR 248). JSR 248 is being pushed ahead of JSR 249 so developers can make the earliest possible start on MSA-compliant applications that will run on the highest-volume mobile devices. JSR 248 has recently been approved, yet, its adoption by OEMs remains to be proved.

  5. A Technical Report (TR-2010-12-10) offering a detailed presentation of the user evaluation tests is available at: http://tr.mguide.gr/TR-2011-03-28.pdf.

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Authors and Affiliations

  1. Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece

    Damianos Gavalas & Michael Kenteris

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  1. Damianos Gavalas
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  2. Michael Kenteris
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Correspondence to Damianos Gavalas.

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Gavalas, D., Kenteris, M. A web-based pervasive recommendation system for mobile tourist guides. Pers Ubiquit Comput 15, 759–770 (2011). https://doi.org/10.1007/s00779-011-0389-x

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