DGCN: Diversified Recommendation with Graph Convolutional Networks

Zheng, Yu; Gao, Chen; Chen, Liang; Jin, Depeng; Li, Yong

doi:10.1145/3442381.3449835

Cited by 91 publications

(39 citation statements)

References 57 publications

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“…Technically, the diversity-oriented recommendation can be divided into post-processing [5] and end-to-end methods [58]. The former diversifies the recommendation lists generated by some models via re-ranking [5,59].…”

Section: Related Workmentioning

confidence: 99%

“…• Coverage. Filter bubbles usually decrease the diversity of recommended items, and thus we incorporate a widely adopted metric for diversity: coverage, which calculates the number of item categories in the recommendation list [58].…”

Section: 21mentioning

confidence: 99%

“…Towards the goal, existing studies prevent the recommendations from merely fitting historical interactions by incorporating additional objectives. For instance, 1) diversity [6,58], which pushes the recommendation list to cover more item categories; 2) fairness [3,30], which pursues fair exposure opportunities over item categories; and 3) calibration [39,48], which ensures that the recommendation list exhibits the same distribution over item categories as the user's history. However, these methods typically make the trade-off across multiple objectives, sacrificing accuracy and even degrading user experience [39,58].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, 1) diversity [6,58], which pushes the recommendation list to cover more item categories; 2) fairness [3,30], which pursues fair exposure opportunities over item categories; and 3) calibration [39,48], which ensures that the recommendation list exhibits the same distribution over item categories as the user's history. However, these methods typically make the trade-off across multiple objectives, sacrificing accuracy and even degrading user experience [39,58]. Moreover, in the feedback loop, users passively adjust the recommendations by user feedback (e.g., click, like, and dislike), which is inefficient and inadequate because users need to constantly provide user feedback until the system recognizes users' intention.…”

Section: Introductionmentioning

confidence: 99%

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User-controllable Recommendation Against Filter Bubbles

Wang,

Feng,

Nie

et al. 2022

Preprint

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Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention.This work proposes a new recommender prototype called User-Controllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles.2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity. CCS CONCEPTS• Information systems → Recommender systems.

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Section: Related Workmentioning

confidence: 99%

Section: 21mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User-controllable Recommendation Against Filter Bubbles

Wang,

Feng,

Nie

et al. 2022

Preprint

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“…In recent years, Graph Neural Networks (GNNs) have been proposed for deep learning on graph data. Due to GNNs' demonstrably powerful ability, they have attracted researchers and developers from a wide range of domains, and have been successfully applied to applications such as recommendation systems [3,6,26,32,40], community question answering [13,38], social network analysis [24,25], and social media understanding [33,34].…”

Section: Introductionmentioning

confidence: 99%

Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning

Hu¹,

Qian²,

Fang³

et al. 2021

Preprint

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Graph Neural Networks (GNNs) have achieved great success in processing graph data by extracting and propagating structureaware features. Existing GNN research designs various propagation schemes to guide the aggregation of neighbor information. Recently the field has advanced from local propagation schemes that focus on local neighbors towards extended propagation schemes that can directly deal with extended neighbors consisting of both local and high-order neighbors. Despite the impressive performance, existing approaches are still insufficient to build an efficient and learnable extended propagation scheme that can adaptively adjust the influence of local and high-order neighbors. This paper proposes an efficient yet effective end-to-end framework, namely Contrastive Adaptive Propagation Graph Neural Networks (CAPGNN), to address these issues by combining Personalized PageRank and attention techniques. CAPGNN models the learnable extended propagation scheme with a polynomial of a sparse local affinity matrix, where the polynomial relies on Personalized PageRank to provide superior initial coefficients. In order to adaptively adjust the influence of both local and high-order neighbors, a coefficient-attention model is introduced to learn to adjust the coefficients of the polynomial. In addition, we leverage self-supervised learning techniques and design a negativefree entropy-aware contrastive loss to explicitly take advantage of unlabeled data for training. We implement CAPGNN as two different versions named CAPGCN and CAPGAT, which use static and dynamic sparse local affinity matrices, respectively. Experiments on graph benchmark datasets suggest that CAPGNN can consistently outperform or match state-of-the-art baselines. The source code is publicly available at https://github.com/hujunxianligong/CAPGNN. CCS CONCEPTS• Information systems → Data mining.

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