Knowledge-Aware Group Representation Learning for Group Recommendation

Deng, Zhiyi; Li, Changyu; Liu, Shujin; Ali, Waqar; Shao, Jie

doi:10.1109/icde51399.2021.00139

“…For example, attentive neural networks are proposed in [3,14] to selectively aggregate user representations within a group, and [36] further captures the fine-grained interactions between group members via a sub-attention network. More recently, there are also studies to incorporate additional information like social connections [4] and knowledge graphs [11] into the learning process of group representations. However, as discussed in Section 1, the point embeddings used in those methods sacrifice the diversity of personal preferences.…”

Section: Preference Aggregationmentioning

confidence: 99%

Thinking inside The Box

Chen

¹

,

Yin

²

,

Long

³

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines. CCS CONCEPTS• Information systems → Recommender systems.

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“…As heterogeneous networks naturally model different types of objects and relationships, recent studies have emerged to exploit the heterogeneity in recommender systems, such as learning representation from rich interactions [39]- [41], learning price-aware recommendations [42] and group recommendations [43], [44] in e-commerce systems. CSE [45] is a unified framework for representation learning.…”

Section: Graph Neural Network For Recommendationsmentioning

confidence: 99%

HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks

Gu

¹

,

Wang

²

,

Wu

³

et al. 2022

2022 IEEE 38th International Conference on Data Engineering (ICDE)

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Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes and edges, there is a bursting research interest in learning expressive node representations in multiplex heterogeneous networks. One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i.e., relationship). Although existing studies utilize explicit metapaths to aggregate neighbors, practically they only consider intra-relationship metapaths and thus fail to leverage the potential uplift by inter-relationship information. Moreover, it is not always straightforward to exploit inter-relationship metapaths comprehensively under diverse relationships, especially with the increasing number of node and edge types. In addition, contributions of different relationships between two nodes are difficult to measure. To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN applies a randomized inter-relationship exploration module to exploit the multiplexity property among different relationships. Then, our model leverages hybrid aggregation flows under intrarelationship metapaths and randomized exploration to learn the rich semantics. To explore the importance of different aggregation flow and take advantage of the multiplexity property, we bring forward a novel hierarchical attention module which leverages both metapath-level attention and relationship-level attention. Extensive experimental results on five real-world datasets suggest that HybridGNN achieves the best performance compared to several state-of-the-art baselines (p < 0.01, t-test) with statistical significance.

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“…As a core ranking mechanism for group-item pairs in CubeRec, we verify the efficacy of such point-to-hypercube distance metric by replacing Eq. (11) with the conventional point distance. That is,…”

Section: Ablation Study (Rq2)mentioning

confidence: 99%

“…For example, attentive neural networks are proposed in [3,14] to selectively aggregate user representations within a and [36] further captures the fine-grained interactions between group members via a sub-attention network. More recently, there are also studies to incorporate additional information like social connections [4] and knowledge graphs [11] into the learning process of group representations. However, as discussed in Section 1, the point embeddings used in those methods sacrifice the diversity of personal preferences.…”

Section: Preference Aggregationmentioning

confidence: 99%

“…(12) does is to: (1) locate the anchor point on G 𝑛 's surface that is the closest to v 𝑗 ; (2) use 𝑓 𝑜𝑢𝑡 (•, •) to measure the outer distance from v 𝑗 to this anchor point; and (3) use 𝑓 𝑖𝑛 (•, •) to measure the inner distance from the centroid c 𝑛 to this anchor point. The outer and inner distances are then combined into 𝑑 𝑛 𝑗 via Eq (11)…”

mentioning

confidence: 99%

See 1 more Smart Citation

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

Chen¹,

Yin²,

Jiang³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines. CCS CONCEPTS• Information systems → Recommender systems.

show abstract

Knowledge-Aware Group Representation Learning for Group Recommendation

Cited by 23 publications

References 26 publications

Thinking inside The Box

Thinking inside The Box

HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

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