Mobile recommender systems in tourism

Gavalas, Damianos; Konstantopoulos, Charalampos; Mastakas, Konstantinos; Pantziou, Grammati

doi:10.1016/j.jnca.2013.04.006

Cited by 393 publications

(200 citation statements)

References 75 publications

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“…Personal recommendations for points of interest can be provided based on the user's proximity of the venue [26]. For context-aware media recommendations on smartphones, the situation of the user (location, activity, time) [20], as well as the device and network capabilities are considered as important contextual parameters [40]. In a news recommendation scenario, the importance of contextualization is demonstrated by the News@hand system [11].…”

Section: Related Workmentioning

confidence: 99%

“…Since users commonly consider the presentation of any information not explicitly requested as intrusive [25], this kind of push-recommendations is not used in our experiment. Pull-based systems, in which the delivery of recommended content is driven by queries, i.e., by user requests, are considered as less intrusive, since users maintain control on information delivery [21].…”

Section: Related Workmentioning

confidence: 99%

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A user-centric evaluation of context-aware recommendations for a mobile news service

Pessemier

Courtois

Vanhecke

et al. 2015

Multimed Tools Appl

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Traditional recommender systems provide personal suggestions based on the user's preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users' usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A user-centric evaluation of context-aware recommendations for a mobile news service

Pessemier

Courtois

Vanhecke

et al. 2015

Multimed Tools Appl

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show abstract

“…A typical user of a travel recommender system is a tourist who is interested in exploring a city and wants to make a tour around (e.g., Yang and Hwang 2013;Borras et al 2014). Such a tour comprises a scheduled list of attractions (museums, heritage sites, shops, parks or other points of specific interest) as well as the trips needed to travel from one point to the other (e.g., Gretzel et al 2004;Gavalas et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimating a latent-class user model for travel recommender systems

Arentze

Kemperman

Aksenov

2018

Inf Technol Tourism

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In determining the selection of sites to visit on a trip tourists have to trade-off attraction values against routing and time-use characteristics of points of interest (POIs). For recommending optimal personalized travel plans an accurate assessment of how users make these trade-offs is important. In this paper we report the results of a study conducted to estimate a user model for travel recommender systems. The proposed model is part of c-Space-a tour-recommender system for tourists on a city trip which uses the LATUS algorithm to find personalized optimal tours. The model takes into account a multi-attribute utility function of POIs as well as dynamic needs of persons on a trip. A stated choice experiment is designed where the current need is manipulated as a context variable and activity choice alternatives are varied. A random sample of 316 individuals participated in the on-line survey. A latent-class analysis shows that significant differences exist between tourists in terms of how they make the trade-offs between the factors and respond to needs. The estimation results provide the parameters of a multi-class user model that can be used for travel recommender systems.

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“…Such multi-user context-aware processes in mobile environments can be found, for example, in roadside, healthcare or disaster relief assistance, the areas of everyday efficiency and planning (price comparison, routing, schedule management on mobile devices), or in the tourism domain (cf. Gavalas, Konstantopoulos, Mastakas, & Pantziou, 2014; Neville et al, 2016; Ventola, 2014; Zhang, Adipat, & Mowafi, 2009). …”

Section: Introductionmentioning

confidence: 99%

Service selection in mobile environments: considering multiple users and context-awareness

Heinrich

Mayer

2018

Journal of Decision Systems

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In mobile environments, users often need to coordinate their actions with other users with regard to user-individual context information like current location when selecting suitable services for a process. Thereby, some users may prefer to conduct particular services together with certain other users. Such multi-user context-aware service selections could result in complex decision problems – making decision support for the participating users highly valuable or even necessary. To do so, we propose an optimisation-based service selection approach for multi-user context-aware processes. We also show how our approach provides decision support by evaluating its efficacy based on a real-world scenario.

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Mobile recommender systems in tourism

Cited by 393 publications

References 75 publications

A user-centric evaluation of context-aware recommendations for a mobile news service

A user-centric evaluation of context-aware recommendations for a mobile news service

Estimating a latent-class user model for travel recommender systems

Service selection in mobile environments: considering multiple users and context-awareness

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