Travelers who access the Internet through currently available web information systems often experience difficulty in selecting interesting products. This is especially true for on-the-move travelers who browse information repositories using mobile devices with poor user interfaces. Travelers suffer from an overload of information and options to consider, and they lack system support in filtering information and comparing products. In this paper, we propose an innovative approach to the problem of mapping travelers ’ needs to a convenient set of travel products or services. In particular, we present an on-the-move restaurant recommender system (mITR) integrated with a pre-travel planning aid system (NutKing). In this approach, firstly, the knowledge contained in a repository of past user choices is exploited to initialize the recommendation process with a set of implicit preferences. Secondly, to minimize user efforts in building a precise search query, we do not require the user to formulate a query at the beginning of the interaction, rather we involve her in a dialogue where the traveler is encouraged to provide critiques and feedback to the system recommendations. These critiques are then incorporated by the system into a new query that tries to better model the user preferences. The approach has been validated empirically by a set of users.

We present an approach to the design of personalized recommender systems that integrates content-based methods, collaborative filtering techniques and case-based reasoning while adopting a user-centered perspective. These techniques are employed to support information search and choice processes. In this framework, we developed and tested a system prototype (NutKing) that helps the user to construct a travel plan by recommending attractive travel products or by proposing complete itineraries. In the information search phase, the system aids the user in specifying a successful query that winnows out unwanted products in electronic catalogues and reduces the information overload. This is accomplished through two kinds of query rewriting operators (relaxation and tightening) in a mixed initiative approach. In the choice phase, the search results are sorted according to a case-base similarity metric, which takes into account the similarity between the users' travel preferences. The aim of this adaptive sorting is to highlight products that are potentially interesting, because they are similar to those selected by other users in an analogous context. The prototype has been empirically evaluated in a pilot study. The results of the pilot evaluation are discussed, with special reference to aspects concerning the usersystem interaction aspects.

This Editorial lead paper for the Journal of Location Based Services surveys this complex and multi-disciplinary field and identifies the key research issues. Although this field has produced early commercial disappointments, the inevitability that pervasive location-aware services on mobile devices will emerge means that much research is needed to inform these developments. The paper reviews firstly: the science and technology of positioning, geographic information science, mobile cartography, spatial cognition and interfaces, information science, ubiquitous computing; and secondly the business, content and legal, social and ethics aspects, before synthesising the key issues for this new field.

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In this paper, we argue that the process of developing travel recommender systems (TRS) can be simplified. By studying the application domain of tourism information systems, and examining the algorithms and architectures available for recommender systems today, we discuss the dependencies and present a methodology for developing TRS, which can be applied at very early stages of TRS development. The methodology aims to be insightful without overburdening the project team with the mathematical basis and technical detail of the state of the art in recommender systems and give guidance on design choices to the project team.

We present ISeller an industry-strength recommendation system for online shopping platforms. The system supports several recommendation paradigms like collaborative, content-based and knowledge-based filtering as well as one-shot and conversational interaction modes. A generic user modelling component allows different forms of hybrid reasoning strategies as well as enables the system to support a process-oriented way of interactive selling in various product domains. This application paper contributes a comprehensive scenario for interactive selling on commercial platforms and thus motivates central user modelling services for recommendation systems. Furthermore, we give an outline on the technical architecture and the implemented system. Our presentation will be illustrated with actually fielded examples from domains of luxury-articles like coffee and cigars.

This article describes a prototype travel recommender system called the Electronic Travel Planner (ETP), which prepares travel itineraries for tourists. The system is driven by models of a traveler’s preferences and requirements, and makes reference to databases containing information pertaining to tourism and travel products. Its main tasks are to select destinations for the traveler to visit, to decide which tours or attractions are to be taken, and to compose a detailed itinerary linking up the chosen components. These tasks entail difficult optimization problems, which the prototype addresses by means of an heuristic problem-solving framework. Computational tests confirm the effectiveness of the methods used, and suggest that an automated approach will be feasible in full-scale travel planning applications.

Mobile applications supporting tourists with travel information can make use of information about the user's location, time and personal preferences to provide personalized recommendations. This could be a solution to the problem of displaying information and navigating on small mobile devices, as it allow tourists to receive information that fit very well with their current situation and needs.However, filtering of information introduce new challenges in terms of facilitating user control and transparency. This project shall through the design and evaluation of a personalized mobile tourism application answer the following How should personalized mobile tourist application be designed to, 1)Allow tourists to easily find information on- and physically locate points of interests? 2)Facilitate transparency and user control in the personalization process? 3)Gather information on the user's preferences in way that is non-obtrusive, accurate and requires little effort from user?

Recommender systems (RS) are employed to personalize user interaction with (e.g. tourism) web-sites, supporting both navigation through large service assortments and the configuration of individual service packages. Depending on the interaction strategy, RS are either utilized to elicit users ’ tastes and preferences or to stimulate desire for different offerings. In addition, as a potentially rich source of digital traces, RS also act as a repository for marketing intelligence. Web-usage mining is an accepted approach to analyse web-usage behaviour based on information traces left by the web-user (Mobasher, 2007). This paper proposes an empirically tested approach which combines typical web-log data with user feedback gathered by an interactive travel advisory system developed for an Austrian spa-resort. The proposed approach focuses on evaluating the RS with respect to efficiency, effectiveness and actionable marketing intelligence.

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