Recommendation to Groups

Jameson, Anthony; Smyth, Barry

doi:10.1007/978-3-540-72079-9_20

Cited by 303 publications

(198 citation statements)

References 24 publications

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“…For instance, see (Jameson and Smyth, 2007;Vassileva and Sun, 2007;Masthoff, 2004;Masthoff and Gatt, 2006;Ardissono et al, 2010). While group modeling has substantially different meanings in the management of synchronous activities, such as the museum visit, or asynchronous ones, such as the participation in a virtual community, an intersection between these two research fields can be found as far as modeling group interests and knowledge is concerned.…”

mentioning

confidence: 99%

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

Ardissono

Kuflik

Petrelli

2011

User Model User-Adap Inter

194

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

194

107

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“…Then, calculating disagreement of item by predictions of group members, disagreement is greatly affected by external trust networks and vice versa. The frame and formula of our method are Figure 1 and formula (1).…”

Section: Grs Calculation Frameworkmentioning

confidence: 99%

“…Group disagreement with the project [1] dis( , ) indicates the degree of difference of users in the group on the predicted score of the project .…”

Section: A Description Of the Aggregation Strategymentioning

confidence: 99%

“…In fact, many daily activities are carried out by a crowd of people from different regions, such as watching movies or TV programs, going to restaurant for meal, and traveling and getting service in public. Therefore, it is necessary for the system to consider suggestion of a certain number of people, which is called GRS (group recommendation systems) [1].…”

Section: Introductionmentioning

confidence: 99%

“…[3] usually generate group preferences through aggregating individual's ratings. According to the paper by Jameson and Smyth [1], the main approaches to generate the preference aggregation are merging the recommendations made for individuals, aggregation of ratings for individuals, and constructing a group preference model. Supan et al [10] present a compilation of the most important preference aggregation techniques.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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A survey of recommendation techniques based on offline data processing

Ren

Zhou

2014

Concurrency and Computation

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Summary Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real‐world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph‐based recommendation and trust‐based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research. Copyright © 2014 John Wiley & Sons, Ltd.

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Recommendation to Groups

Cited by 303 publications

References 24 publications

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

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

Group Recommendation Systems Based on External Social‐Trust Networks

A survey of recommendation techniques based on offline data processing

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