Group recommendation

Amer-Yahia, Sihem; Roy, Senjuti Basu; Chawlat, Ashish; Das, Gautam; Yu, Cong

doi:10.14778/1687627.1687713

Cited by 310 publications

(51 citation statements)

References 9 publications

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“…There is a wide range of applications for group profiling, like understanding social structures [9], network visualization, recommender systems [1,4,8], and direct marketing [3]. One of the important applications of group profiling is in the content customization problem.…”

Section: Chiir'16 March 13-17 2016 Carrboro Nc Usamentioning

confidence: 99%

Generalized Group Profiling for Content Customization

Dehghani

Azarbonyad

Kamps

et al. 2016

Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

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There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile sharing one or more characteristics with the user at hand. Personal profiles are often sparse, due to cold start problems and the fact that users typically search for new items or information, necessitating to back-off to customization, but group profiles often suffer from accidental features brought in by the unique individual contributing to the group. In this paper we propose a generalized group profiling approach that teases apart the exact contribution of the individual user level and the 'abstract' group level by extracting a latent model that captures all, and only, the essential features of the whole group.Our main findings are the followings. First, we propose an efficient way of group profiling which implicitly eliminates the general and specific features from users' models in a group and takes out the abstract model representing the whole group. Second, we employ the resulting models in the task of contextual suggestion. We analyse different grouping criteria and we find that group-based suggestions improve the customization. Third, we see that the granularity of groups affects the quality of group profiling. We observe that grouping approach should compromise between the level of customization and groups' size.

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Section: Chiir'16 March 13-17 2016 Carrboro Nc Usamentioning

confidence: 99%

Generalized Group Profiling for Content Customization

Dehghani

Azarbonyad

Kamps

et al. 2016

Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

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“…Another direction is to achieve the agreement within group based on a certain consensus function, like, [4] captures the social, expertise, and interest dissimilarity among multiple group members. Indeed, some other factors might also be considered during the agreement process, e.g., [16] analyzed that how the personality of cooperation and trust could influence the group recommendation results, and [1] discussed the monotonicity and efficiency for group recommendation. Finally, some prior works, e.g.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Impact of Dynamic Mutual Influence on Social Event Participation

Zhong

Zhu

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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Nowadays, it is commonly seen that an offline social event is organized through online social network services (SNS), in this way cyber strangers can be connected in physical world. While there are some preliminary studies on social event participation through SNS, they usually have more focus on the mining of event profiles and have less focus on the social relationships among target users. In particular, the importance of dynamic mutual influence among potential event participants has been largely ignored. In this paper, we develop a novel discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a variant two-stage discriminant framework to capture the users' preferences as well as their latent social connections. The experimental results on real-world data show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines, which validates the hypothesis that dynamic mutual influence could play an important role in the decisionmaking process of social event participation.

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“…In addition to personal recommendations, there are contexts in which people operate in groups, and so, a model for group recommendations should be defined. Some approaches have been recently proposed towards this direction (e.g., [2]). …”

Section: The Grecs Group Recommendations Frameworkmentioning

confidence: 99%

gRecs: A Group Recommendation System Based on User Clustering

Ntoutsi

Stefanidis

Nørvåg

et al. 2012

Lecture Notes in Computer Science

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Abstract. In this demonstration paper, we present gRecs, a system for group recommendations that follows a collaborative strategy. We enhance recommendations with the notion of support to model the confidence of the recommendations. Moreover, we propose partitioning users into clusters of similar ones. This way, recommendations for users are produced with respect to the preferences of their cluster members without extensively searching for similar users in the whole user base. Finally, we leverage the power of a top-k algorithm for locating the top-k group recommendations.

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Group recommendation

Cited by 310 publications

References 9 publications

Generalized Group Profiling for Content Customization

Generalized Group Profiling for Content Customization

Exploring the Impact of Dynamic Mutual Influence on Social Event Participation

gRecs: A Group Recommendation System Based on User Clustering

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