Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices

Ardissono, Liliana; Goy, Anna; Petrone, G.; Segnan, Marino; Torasso, Pietro

doi:10.1080/713827254

Cited by 304 publications

(185 citation statements)

References 21 publications

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“…The new opportunity was explored by INTRIGUE (Ardissono et al, 2003), which supported browsing cultural heritage information and planning tours that met the requirements of heterogeneous user groups, such as families with elderly members and children. The content (text and images) was dynamically selected, arranged and displayed on web pages tailored for either a desktop browser or a WAP mini-browser.…”

mentioning

confidence: 99%

Personalization in cultural heritage: the road travelled and the one ahead

Ardissono

Kuflik

Petrelli

2011

User Model User-Adap Inter

Self Cite

196

107

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mentioning

confidence: 99%

Personalization in cultural heritage: the road travelled and the one ahead

Ardissono

Kuflik

Petrelli

2011

User Model User-Adap Inter

Self Cite

196

107

View full text Add to dashboard Cite

“…These strategies can be also used for combining individual recommendations into group recommendations. Mostly used aggregation strategies for group recommendations are least misery (O'Connor et al 2001), average (Ardissono et al 2003), and multiplicative (Masthoff 2004).…”

Section: Aggregation Strategiesmentioning

confidence: 99%

“…Some early systems were developed in a variety of domains, such as, group web page recommendation (Lieberman et al 1999), tour packages for groups of tourists (Ardissono et al 2003), music tracks and playlists for large groups of many listeners (Crossen et al 2002), movies and TV programs for friends and family (O'Connor et al 2001;Yu et al 2006). Group scenarios are especially popular in the food domain in which a group of family members, friends or colleagues wants to make a party or simply have a meal together.…”

Section: Introductionmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

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Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

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“…In addition to personal information, there are some other pieces of information which would be taken into account [28,29], and in another interesting content-based recommendation system there is Pocket Restaurant Finder [30], which recommends restaurants for groups of people based on user location and the culinary characteristics of the restaurant. McCarthy proposes the Pocket Restaurant Finder that recommends restaurants to groups of people considering their culinary preferences and location.…”

Section: Group Recommendation Systems Grsmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of social networks and online mobile communities, group recommendation systems support users' interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users' personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external socialtrust networks is proposed, which builds a group profile by analyzing not only users' preferences, but also the social relationships between members inside and outside of the group. We employ the users' degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

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Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices

Cited by 304 publications

References 21 publications

Personalization in cultural heritage: the road travelled and the one ahead

Personalization in cultural heritage: the road travelled and the one ahead

An overview of recommender systems in the healthy food domain

Group Recommendation Systems Based on External Social‐Trust Networks

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