Group Recommendation with Latent Voting Mechanism

Guo, Lei; Yin, Hongzhi; Wang, Qinyong; Cui, Bin; Huang, Zi; Cui, Lizhen

doi:10.1109/icde48307.2020.00018

Cited by 47 publications

(43 citation statements)

References 31 publications

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“…But the interactions among group members are ignored. To address the above challenges, Guo et al [25] developed a group selfattention neural network to model the social influence and interactions among group members simultaneously. He et al [30] modeled the interactions among groups, users, and items with an interaction graph, and then learned their multi-view embeddings.…”

Section: Early Aggregation Methodsmentioning

confidence: 99%

“…Generally, groups can be divided into persistent groups and occasional groups [8,25,65] based on whether they have stable group members. Concretely, Persistent Groups (PGs) [31,47,55] (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

“…For this kind of groups, we can directly apply the recommendation methods designed for individual users [11,24,28,58] by treating each group as a pseudo user, since there are sufficient persistent groupitem interactions. Occasional Groups (OGs) [4,25,39,68] (a.k.a. cold-start group) refer to groups that are casually formed by ad-hoc users.…”

Section: Introductionmentioning

confidence: 99%

“…To address the above challenges, some advanced data-driven aggregation methods [8,25,65] have been proposed to learn the group representation. For example, Cao et al [8] incorporated the attention mechanism to dynamically aggregate different group members' preference information.…”

Section: Introductionmentioning

confidence: 99%

“…But they ignore that the group's final decision is usually reached through consensus among group members, and the social interactions are unexplored. To address this limitation, Guo et al [25] treated the group representation learning process as a multiple voting step, and developed a stacked social self-attention network [53] to aggregate group members' preferences. To alleviate the sparsity issue of group-item interaction data, the users' individual activity data has been leveraged to complement the group recommendation task in their method.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Guo

Yin

Chen

et al. 2021

ACM Trans. Inf. Syst.

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Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.

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Section: Early Aggregation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Guo

Yin

Chen

et al. 2021

ACM Trans. Inf. Syst.

Self Cite

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Self‐supervised graph learning for occasional group recommendation

Hao¹,

Yin²,

Zhang³

et al. 2022

Int J of Intelligent Sys

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As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold‐start group) has no or few historical interacted items. As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high‐quality group representations. The recent proposed Graph Neural Networks (GNNs), which incorporate the high‐order neighbors of the target occasional group, can alleviate the above problem in some extent. However, these GNNs still can not explicitly strengthen the embedding quality of the high‐order neighbors with few interactions. Motivated by the self‐supervised learning technique, which is able to find the correlations within the data itself, we propose a self‐supervised graph learning framework, which takes the user/item/group embedding reconstruction as the pretext task to enhance the embeddings of the cold‐start users/items/groups. To explicitly enhance the high‐order cold‐start neighbors' embedding quality, we further introduce an embedding enhancer, which leverages the self‐attention mechanism to improve the embedding quality for them. Comprehensive experiments show the advantages of our proposed framework than the state‐of‐the‐art methods.

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Dynamic Conflict Resolution of IoT Services in Smart Homes

Chaki

Bouguettaya

2021

Lecture Notes in Computer Science

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Group Recommendation with Latent Voting Mechanism

Cited by 47 publications

References 31 publications

Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Self‐supervised graph learning for occasional group recommendation

Dynamic Conflict Resolution of IoT Services in Smart Homes

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