DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation

Wang, Huizhao; Liu, Guanfeng; Liu, An; Li, Zhixu; Zheng, Kai

doi:10.24963/ijcai.2019/513

Cited by 24 publications

(8 citation statements)

References 7 publications

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“…The later infers all possible sequences of user choices over all items, which may suffer from intractable computation problem for real-world applications where the number of items is large. Recently, many deep learning based approaches are proposed for the task, which make use of pairwise item-transition information to model the user preference of a given session [2,4,6,18,19,21]. These approaches have achieved encouraging results, but they still face the following issues.…”

Section: Introductionmentioning

confidence: 99%

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Wang

Wei

Cong

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

442

216

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Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the other sessions, which may contain both relevant and irrelevant item-transitions to the current session. This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session. Specifically, GCE-GNN learns two levels of item embeddings from session graph and global graph, respectively: (i) Session graph, which is to learn the session-level item embedding by modeling pairwise item-transitions within the current session; and (ii) Global graph, which is to learn the global-level item embedding by modeling pairwise item-transitions over all sessions. In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. We also design a session-level item representation learning layer, which employs a GNN on the session graph to learn session-level item embeddings within the current session. Moreover, GCE-GNN aggregates the learnt item representations in the two levels with a soft attention mechanism. Experiments on three benchmark datasets demonstrate that GCE-GNN outperforms the state-of-the-art methods consistently.

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Section: Introductionmentioning

confidence: 99%

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Wang

Wei

Cong

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

442

216

View full text Add to dashboard Cite

show abstract

“…Cong et al [42] distinguish the importance of reviews at both word level and sentence level using a hierarchical attention-based network for e-commerce recommendation. Similarly, Wang et al [43] proposed a twolevel attention mechanism with time encoding and achieved good performance in the next-item recommendation.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Attentional Factorization Machines for Expert Recommendation in Community Question Answering

Tang

et al. 2020

IEEE Access

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The most challenging task of Community Question Answering (CQA) is to provide high-quality answers to users' questions. Currently, a variety of expert recommendation methods have been proposed and greatly improved the effective matching between questions and potential good answerers. However, the performance of existing methods can be adversely affected by many common factors such as data sparsity and noise problem, which cause less precise user modeling. Moreover, existing methods often model user-question interactions through simple ways, failing to capture the multiple scale interactions of question and answerers, which make it difficult to find answerers who are able to provide the best answers. In this paper, we propose an attention-based variant of Factorization Machines (FM) called Hierarchical Attentional Factorization Machines (HaFMRank) for answerer recommendation in CQA, which not only models the interactions between pairs of individual features but emphasizes the roles of crucial features and pairwise interactions. Specifically, we introduce the within-field attention layer to capture the inner structure of features belonging to the same field, while a feature-interaction attention layer is adopted to examine the importance of each pairwise interaction. A pre-training procedure is designed to generate latent FM feature embedding that encode question context and user history into the training process of HaFMRank. The performance of the proposed HaFMRank is evaluated by using real-world datasets of Stack Exchange and experimental results demonstrate that it outperforms several state-of-the-art methods in best answerer recommendation.

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“…With the rapid development of deep learning technology, many efforts have been devoted to obtaining personalized user interests in the field of recommendation [12,17,19,21,35,36,38,42,43], especially the combination of recurrent neural networks (RNN) [8,22] and attention mechanisms [4,30], which has made great progress in capturing long-term and short-term user preferences and learning diverse interests. For example, Chen et al [2] proposed a hierarchical attention network at category-level and item-level for long-term and short-term interest modeling in micro-video click-through prediction.…”

Section: Introductionmentioning

confidence: 99%

“…• Multi-scale time effects. Previous methods usually consider that the effect of micro-videos on user interest modeling decreases over time implicitly, which is captured by RNN [8,10,11] or learned from timestamp features [20,30]. However, they ignore the case that the importance of micro-videos decreases over time varies from user to user, that is to say, for different users, 1 https://www.jiguang.cn/reports/43.…”

Section: Introductionmentioning

confidence: 99%

What Aspect Do You Like

Jiang¹,

Wang

Wei³

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Online micro-video recommender systems aim to address the information explosion of micro-videos and make the personalized recommendation for users. However, the existing methods still have some limitations in learning representative user interests, since the multi-scale time effects, user interest group modeling, and false positive interactions are not taken into consideration. In view of this, we propose an end-to-end Multi-scale Time-aware user Interest modeling Network (MTIN). In particular, we first present an interest group routing algorithm to generate fine-grained user interest groups based on user's interaction sequence. Afterwards, to explore multi-scale time effects on user interests, we design a time-aware mask network and distill multiple temporal information by several parallel temporal masks. And then an interest mask network is introduced to aggregate fine-grained interest groups and generate the final user interest representation. At last, in the prediction unit, the user representation and micro-video candidates are fed into a deep neural network (DNN) for predictions. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, and the experimental results demonstrate that our proposed model achieves substantial gains over the state-of-the-art methods.

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DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation

Cited by 24 publications

References 7 publications

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

Hierarchical Attentional Factorization Machines for Expert Recommendation in Community Question Answering

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