Time-aware sequence model for next-item recommendation

Wang, Dongjing; Xu, Dengwei; Yu, Dongjin; Xu, Guandong

doi:10.1007/s10489-020-01820-2

Cited by 29 publications

(7 citation statements)

References 34 publications

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“…Yuan et al [38] inputted four kinds of context information, including input context, correlation context, static interest context and transition context, into the GRU framework through redefined update gates and reset gates. Wang et al [24] proposed a recommendation model based on Long Short-Term Memory (LSTM, variation of RNN), which employed time interval and duration information to obtain user's interest. Wu et al [31] projected context information into a uniform latent vector space and then fused them into the RNN model by means of three combinations including add, stack, and multilayer perception.…”

Section: Context-aware Recommendationsmentioning

confidence: 99%

Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

Zhao

Cao²,

Lv³

et al. 2023

ITC

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The graph neural network (GNN) based approaches have attracted more and more attention in session-based recommendation tasks. However, most of the existing methods do not fully take advantage of context information in session when capturing user’s interest, and the research on context adaptation is even less. Furthermore, hypergraph has potential to express complex relations among items, but it has remained unexplored. Therefore, this paper proposes an adaptive context-embedded hypergraph convolutional network (AC-HCN) for session-based recommendation. At first, the data of sessions is constructed as session hypergraph. Then, the representation of each item in session hypergraph is learned using an adaptive context-embedded hypergraph convolution. In the convolution, different types of context information from both current item itself and the item’s neighborhoods are adaptively integrated into the representation updating of current item. Meanwhile, an adaptive transformation function is employed to effectively eliminate the effects of irrelevant items. Then, the learned item representations are combined with time interval embeddings and reversed position embeddings to fully reflect time interval information and sequential information between items in session. Finally, based on learned item representations in session, a soft attention mechanism is used to obtain user’s interest, and then a recommendation list is given. Extensive experiments on the real-world datasets show that the proposed model has significantly improvement compared with the state-of-arts methods.

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Section: Context-aware Recommendationsmentioning

confidence: 99%

Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

Zhao

Cao²,

Lv³

et al. 2023

ITC

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“…Moreover, Doan et al [28] design a deep long short term memory RNN model with attention mechanism to capture both the sequential, and temporal/spatial characteristics into its learned representations for successive POI recommendation. Recently, Wang et al [29] propose a novel next-item recommendation model which leverages interval and duration gated LSTM to accurately capture users' long-term and short-term preferences. Although the RNN and its variants are quite effective in sequential modeling problems, they lack the ability of fusing multi-dimensional relations in heterogeneous LBSN.…”

Section: Neural Network Based Recommendationmentioning

confidence: 99%

Deep Pairwise Learning for User Preferences Via Dual Graph Attention Model in Location-Based Social Networks

Gong,

Zheng,

Zhang

et al. 2022

SSRN Journal

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“…Using the micro profile technique, they use a Kalman filter to forecast user preference vectors from user characteristics. Time is widely implemented in the multimedia domain [23,24,[30][31][32][33][34]52] and learning domain [28, 35-37] [25, 26] according to the literature. However, in the e-commerce field, it is rarely utilized to study purchase data rather than just rating data.…”

Section: Related Workmentioning

confidence: 99%

Exponential Decay Function-Based Time-Aware Recommender System for e-Commerce Applications

Hassan¹,

Fadel²,

Akkari³

2022

IJACSA

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Unlike traditional recommendation systems that rely only on the user's preferences, context-aware recommendation systems (CARS) consider the user's contextual information such as (time, weather, and geographical location). These data are used to create more intelligent and effective recommendation systems. Time is one of the most important and influential factors that affect users' preferences and purchasing behavior. Thus, in this paper, time-aware recommendation systems are investigated using two common methods (Bias and Decay) to incorporate the time parameter with three different recommendation algorithms known as Matrix Factorization, K-Nearest Neighbor (KNN), and Sparse Linear Method (SLIM). The performance study is based on an e-commerce database that includes basic user purchasing actions such as add to cart and buy. Results are compared in terms of precision, recall, and Mean Average Precision (MAP) parameters. Results show that Decay-MF and Decay-SLIM outperform the Bias CAMF and CA-SLIM. On the other hand, Decay-KNN reduced the accuracy of the RS compared to the context-unaware KNN.

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Time-aware sequence model for next-item recommendation

Cited by 29 publications

References 34 publications

Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

Adaptive Context-Embedded Hypergraph Convolutional Network for Session-based Recommendation

Deep Pairwise Learning for User Preferences Via Dual Graph Attention Model in Location-Based Social Networks

Exponential Decay Function-Based Time-Aware Recommender System for e-Commerce Applications

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