Exploring personal impact for group recommendation

Liu, Xingjie; Tian, Yuan; Ye, Mao; Lee, Wang-Chien

doi:10.1145/2396761.2396848

Cited by 129 publications

(89 citation statements)

References 18 publications

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“…Several model-based methods have been proposed for group recommendation. Social influences [23,11,8,9] have been explored. For example, Liu et al [11] proposed a personal impact topic (PIT) model for group recommendation and an extended model with social information.…”

Section: Group Recommendationmentioning

confidence: 99%

“…Social influences [23,11,8,9] have been explored. For example, Liu et al [11] proposed a personal impact topic (PIT) model for group recommendation and an extended model with social information. PIT builds an author-topic model and differentiates personal impacts for group decision by introducing a personal impact variable to represent the relative probability to influence the group's decision in the PIT model.…”

Section: Group Recommendationmentioning

confidence: 99%

“…We used two popular metrics for evaluating the quality of our model and its competitors, namely Precision@N and Recall@N [11,26,2]. Precision@N is the number of correctly predicted locations divided by the total number N of recommendations made.…”

Section: Experiments Metricsmentioning

confidence: 99%

“…These approaches are shown to be inadequate because they ignore the influences between users in the group decision. Recently, several model-based group recommendation methods have been proposed [23,11,26], which consider social influences. However, although location information has been shown to play an important role in location recommendation [12,25], past studies on group recommendation do not explicitly con-sider the problem of venue recommendation to a group and do not model the geographical influences of users.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

Lu¹,

Li²,

Mamoulis³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Location-based social networks such as Foursquare and Plancast have gained increasing popularity. On those sites, users can organize and participate in group activities; hence, recommending venues to a group is of practical importance. In this paper, we study the problem of recommending venues to groups of users and propose a Hierarchical Bayesian Model (HBGG) for this purpose. First, a generative group geographical topic model (GG) which exploits group membership, group mobility regions and group preferences is proposed. And we integrate social structure into oneclass collaborative filtering as social-based collaborative filtering (SOCF) to leverage social wisdom. Through the shared latent group features, HBGG connects the group geographical model with SOCF framework for group recommendation. Experimental results on two real datasets show that our methods outperforms the state-of-the-art group recommenders, especially on cold-start user groups.

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Section: Group Recommendationmentioning

confidence: 99%

Section: Group Recommendationmentioning

confidence: 99%

Section: Experiments Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

Lu¹,

Li²,

Mamoulis³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…As pointed out in [16], to obtain adequate group recommendations it is not only necessary to model users' individual preferences, but also to understand how a decision among group members is reached. While research on group decision making [31] is concerned with collaboratively making choices, focusing on the social process and the outcome, these aspects have mostly not been addressed in the development of group recommender systems.…”

Section: Related Workmentioning

confidence: 99%

Preference Elicitation and Negotiation in a Group Recommender System

Márquez

Ziegler

2015

Human-Computer Interaction – INTERACT 2015

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Abstract. We present a novel approach to group recommender systems that better takes into account the social interaction in a group when formulating, discussing and negotiating the features of the item to be jointly selected. Our approach provides discussion support in a collaborative preference elicitation and negotiation process. Individual preferences are continuously aggregated and immediate feedback of the resulting recommendations is provided. We also support the last stage in the decision process when users collectively select the final item from the recommendation set. The prototype hotel recommender Hootle is developed following these concepts and tested in a user study. The results indicate a higher overall satisfaction with the system as well as a higher perceived recommendation quality when compared against a system version where no negotiation was possible. However, they also indicate that the negotiation-based approach may be more suitable for smaller groups, an aspect that will require further research.

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Adaptive group‐aware topic model for venue recommendation

Meng

Zhang

2022

Concurrency and Computation

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SummaryVenue recommendation plays a vital role in helping the user discovers interesting locations. However, most of the existing studies have never considered the case of multiple people visiting a venue together, such as inviting friends to dinner, so they ignore the influence of group preference on making decisions. Furthermore, the participants' influence on the inviter's decision is also affected by participation time, venue category, and visiting sequence. In this article, we propose an adaptive group‐aware topic model (AGATM) to provide TOP‐K venue recommendations. The model considers the individual's personalized preferences and group preferences at different times to provide venue recommendations by simulating the process of making decisions for venues. We conduct extensive experiments on two real datasets to evaluate the performance of our proposed method. The experimental results show that our model outperforms existing benchmark algorithms.

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Exploring personal impact for group recommendation

Cited by 129 publications

References 18 publications

HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

HBGG: a Hierarchical Bayesian Geographical Model for Group Recommendation

Preference Elicitation and Negotiation in a Group Recommender System

Adaptive group‐aware topic model for venue recommendation

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