A hierarchical contextual attention-based network for sequential recommendation

Cui, Qiang; Wu, Shu; Huang, Yan; Wang, Liang

doi:10.1016/j.neucom.2019.04.073

Cited by 44 publications

(28 citation statements)

References 25 publications

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“…For example, a Gated Recurrent Unit (GRURec) architecture with a ranking loss was proposed by [15] for session-based recommendation. In the follow-up papers, various RNN variants have been designed to extend the typical one for different application scenarios, such as by adding personalization [25], content [9] and contextual features [27], attention mechanism [7,20] and different ranking loss functions [14].…”

Section: Related Workmentioning

confidence: 99%

A Simple Convolutional Generative Network for Next Item Recommendation

Yuan

Karatzoglou

Arapakis

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

442

340

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Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short-and long-range item dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback. ACM Reference Format:

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Section: Related Workmentioning

confidence: 99%

A Simple Convolutional Generative Network for Next Item Recommendation

Yuan

Karatzoglou

Arapakis

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

442

340

View full text Add to dashboard Cite

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“…Other methods based on Convolutional Neural Networks (CNN) [40], Memory Network [5] and Attention Models [22] have also been explored. The hierarchical structure generalized from RNN, Attention or CNN based models [7,31,45] is used to model transitions inter-and intra-sessions. The recent work [45] by You et al showed that using Temporal Convolutional Network to encode and decode session-level information and GRU for user-level transition is the most effective hierarchical structure.…”

Section: Sequential Recommendationmentioning

confidence: 99%

“…Concerning such sequential dependence within user preferences, the task of sequential recommendation is set to predict the ongoing relevant items based on a sequence of the user's historical actions. Such setting has been widely studied [4,5,7,16,19,31,40,45] and practiced in popular industry recommender systems such as YouTube [3,39] and Taobao [38]. Take the online shopping scenario illustrated in Figure 1 for example: the system is given a series of user behavior records and needs to recommend the next set of items for the user to examine.…”

mentioning

confidence: 99%

“…It is shown in [16,31] that the pattern of user preference transition usually differs after a session gap, which is commonly defined as a minimal of 30 minutes' inactivity. Prior work has demonstrated many effective hierarchical neural network structures, such as hierarchical recurrent neural networks (RNN) [7,31,45] to jointly model the transition patterns in-and cross-sessions. However, the strength stemmed from its session assumption could also be the bottleneck of session based recommendation, that is, the user preference does not necessarily transit in strict accordance with the manually defined session boundaries.…”

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confidence: 99%

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Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Cai

Wang

2020

Proceedings of the Web Conference 2020

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Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction.In this paper, we argue that the influence from the past events on a user's current action should vary over the course of time and under different context. Thus, we propose a Contextualized Temporal Attention Mechanism that learns to weigh historical actions' influence on not only what action it is, but also when and how the action took place. More specifically, to dynamically calibrate the relative input dependence from the self-attention mechanism, we deploy multiple parameterized kernel functions to learn various temporal dynamics, and then use the context information to determine which of these reweighing kernels to follow for each input. In empirical evaluations on two large public recommendation datasets, our model consistently outperformed an extensive set of state-of-the-art sequential recommendation methods.

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“…However, in recent years, due to the increasing amount of data, the limitations of traditional algorithms have become more and more visible, and Neural Networks have once again been pushed highly to the research. Network structures such as Long Short-Term Memory (LSTM) [10][11][12], Gated Recurrent Unit (GRU) [13,14], and Recurrent Neural Networks (RNNs) [15] have been widely used in user behavior serialization modeling problems and personalized recommendation [15,16].…”

Section: Introductionmentioning

confidence: 99%

Nonindependent Session Recommendation Based on Ordinary Differential Equation

Yang

Zhang

Liu

et al. 2020

Mathematical Problems in Engineering

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The recommendation method based on user sessions is mainly to model sessions as sequences in the assumption that user behaviors are independent and identically distributed, and then to use deep semantic information mining through Deep Neural Networks. Nevertheless, user behaviors may be a nonindependent intention at irregular points in time. For example, users may buy painkillers, books, or clothes for different reasons at different times. However, this has not been taken seriously in previous studies. Therefore, we propose a session recommendation method based on Neural Differential Equations in an attempt to predict user behavior forward or backward from any point in time. We used Ordinary Differential Equations to train the Graph Neural Network and could predict forward or backward at any point in time to model the user's nonindependent sessions. We tested for four real datasets and found that our model achieved the expected results and was superior to the existing session-based recommendations.

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A hierarchical contextual attention-based network for sequential recommendation

Cited by 44 publications

References 25 publications

A Simple Convolutional Generative Network for Next Item Recommendation

A Simple Convolutional Generative Network for Next Item Recommendation

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Nonindependent Session Recommendation Based on Ordinary Differential Equation

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