Neural Attentive Session-based Recommendation

Li, Jing; Ren, Pengjie; Chen, Zhumin; Zhang, Ren; Lian, Tao; Ma, Jun

doi:10.1145/3132847.3132926

Cited by 1,179 publications

(936 citation statements)

References 37 publications

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“…Therefore, accurate recommender system is not only essential for the quality of service, but also the profit of the service provider. One such system should exploit the rich side information beyond user-item interactions, such as content-based (e.g., user attributes and product image features ), context-based (e.g., where and when a purchase is made [Rendle et al, 2011;), and sessionbased (e.g., the recent browsing history of users [Li et al, 2017;Bayer et al, 2017]). However, existing collaborative filtering (CF) based systems merely rely on user and item features (e.g., matrix factorization based [He et al, 2016] and the recently proposed neural collaborative filtering methods Bai et al, 2017]), which are far from sufficient to capture the complex decision psychology of the setting and the mood of a user behavior .…”

Section: Introductionmentioning

confidence: 99%

Discrete Factorization Machines for Fast Feature-based Recommendation

Liu

Feng

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10 7 , results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two realworld datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss.

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Section: Introductionmentioning

confidence: 99%

Discrete Factorization Machines for Fast Feature-based Recommendation

Liu

Feng

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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“…Heuristics methods. We include some simple heuristic methods, which show strong performance in prior sequential recommendation work [22,31].…”

Section: Baseline Methodsmentioning

confidence: 99%

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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“…Following that, [26] improves the model with data augmentation and the consideration of temporal user behavior shift. In addition to using RNN, [13] also adopts attention mechanism to capture a user's sequential behavior and its main purpose in a current session. Similarly, [14] proposes a novel attention mechanism to capture both the users' long-term interests in general and their short-term attention.…”

Section: Related Workmentioning

confidence: 99%

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Wang

Zhang

Liu

et al. 2020

Proceedings of the Web Conference 2020

110

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Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based behavior prediction leverage powerful representation learning approaches to encode items' sequential relevance in a low-dimensional space, they suffer from several limitations. Firstly, they focus on only utilizing the same type of user behavior for prediction, but ignore the potential of taking other behavior data as auxiliary information. This is particularly crucial when the target behavior is sparse but important (e.g., buying or sharing an item). Secondly, item-to-item relations are modeled separately and locally in one behavior sequence, and they lack a principled way to globally encode these relations more effectively. To overcome these limitations, we propose a novel Multirelational Graph Neural Network model for Session-based target behavior Prediction, namely MGNN-SPred for short. Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types. Based on MRIG, MGNN-SPred learns global itemto-item relations and further obtains user preferences w.r.t. current target and auxiliary behavior sequences, respectively. In the end, MGNN-SPred leverages a gating mechanism to adaptively fuse user representations for predicting next item interacted with target behavior. The extensive experiments on two real-world datasets demonstrate the superiority of MGNN-SPred by comparing with state-of-the-art session-based prediction methods, validating the benefits of leveraging auxiliary behavior and learning item-to-item relations over MRIG.

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Neural Attentive Session-based Recommendation

Cited by 1,179 publications

References 37 publications

Discrete Factorization Machines for Fast Feature-based Recommendation

Discrete Factorization Machines for Fast Feature-based Recommendation

Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

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