Map-Based Interaction with a Conversational Mobile Recommender System

Averjanova, Olga; Ricci, Francesco; Nguyen, Quoc‐Hung

doi:10.1109/ubicomm.2008.16

Cited by 52 publications

(28 citation statements)

References 11 publications

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“…Finally, we give our conclusions and indicate some avenues for future research. We note that this chapter extends (Averjanova et al, 2008) that more briefly describes MapMobyRek and its validation.…”

Section: Introductionmentioning

confidence: 68%

Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

Ricci

Nguyen

Averjanova³

2010

Tourism Informatics

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Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems (RSs) are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e., a well chosen selection of the items contained in the repository), adapting this selection to the user's needs and preferences in a particular usage context. In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on "critiquing" with map visualization technologies to build a new mapbased conversational mobile RS that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors' real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system's recommendation effectiveness, and increases the user satisfaction.

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Section: Introductionmentioning

confidence: 68%

Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

Ricci

Nguyen

Averjanova³

2010

Tourism Informatics

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“…In mobilizing the recommenders designed to operate on mobile devices and usage contexts [59]. Mobile computing is emerging as the most natural platform for personal computing.…”

Section: The Challenges and Limitations Of Recommendation Systemsmentioning

confidence: 99%

A Literature Review of Empirical Studies of Recommendation Systems

Akhtar¹,

Agarwal²

2015

IJAIS

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In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in ecommerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user's needs and preferences, and UtilityBased Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.

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“…[1][2] [3]). The examples of LBMSs range from emergency services to city guides that provide information about the local tourist attractions and entertainment premises such as restaurants and bars.…”

Section: Introductionmentioning

confidence: 99%

“…The examples of LBMSs range from emergency services to city guides that provide information about the local tourist attractions and entertainment premises such as restaurants and bars. Usually, the entertainment-oriented LBMSs utilize a mapbased interface for visualizing the possible points of interest and the related information [1] [3].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the recommender systems can be defined as information search and decision support tools that provide personalized recommendations according to the user's preferences and the usage contexts [3] [6]. The usage context is usually characterized by the hour of the day, the day of the week and the location of the user, whereas the user's preferences are typically expressed with versatile profiles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

User Experience in Added Value Location-Based Mobile Music Service

Koskela

Järvinen

Liu

et al. 2010

2010 IEEE International Conference on Web Services

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Abstract-In this paper, the user experience in added value location-based mobile music service called the City Night Life (CNL) is evaluated and analyzed. The CNL service provides recommendations about entertainment premises in the proximity by matching their music styles with the user's personal music preferences. Entertainment premises are shown on a map interface and are marked with colors according to their match. The paper presents the components of the CNL service, the music profile, matching algorithm and the map-based user interface in a web browser. The user evaluation with 53 test users assessed the feasibility and business potential of the CNL service. Data was collected with both quantitative questionnaires and short interviews. The results indicate that the CNL service will be most useful in relatively large, unfamiliar cities. Furthermore, the matching algorithm was perceived to perform quite well.

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Map-Based Interaction with a Conversational Mobile Recommender System

Cited by 52 publications

References 11 publications

Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

A Literature Review of Empirical Studies of Recommendation Systems

User Experience in Added Value Location-Based Mobile Music Service

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