STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation

By Qiao Liu, Yifu Zeng, Refuoe Mokhosi, Haibin Zhang. Knowledge Discovery and Data Mining, 2018

1,031 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fd3f489fc0438e500c0473af40dfebe4705df6d2

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In Computer Science Applications

In Information Systems

In Management Science and Operations Research

Citations in the corpus

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, neutral): “STAMP [38] SIGKDD 2018 YOOCHOOSE, DIGINETICA”
Evaluation of session-based recommendation algorithms (2018, methodology): “We performed an initial evaluation of the STAMP method proposed in Liu et al. (2018).”
Deep Session Interest Network for Click-Through Rate Prediction (2019, methodology): “Besides RNNs, [ Liu et al. , 2018; Kang and McAuley, 2018 ] apply only self-attention based models to effectively capture long-term and short-term interests of a session.”
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, methodology): “• STAMP (Liu et al. 2018): employs the self-attention mechanism to enhance session-based recommendation.”
Artificial intelligence in recommender systems (2020, background): “The short-term user preference is enhanced in [100] and user preference drift is also taken into consideration.”
A Survey on Session-based Recommender Systems (2019, methodology): “Session-based Recommendation with Graph Neural Networks (SR-GNN) [129] is the pioneering work that introduced GNN into SBRSs and is claimed to have achieved superior performance, compared with non-GNN approaches including Short-Term Attention/Memory Priority Model (STAMP) [58], Neural Attentive Reco”
SSE-PT: Sequential Recommendation Via Personalized Transformer (2020, methodology): “[21] only uses fully connected layers with one attention block that is not self-attentive.”
Graph Neural Networks in Recommender Systems: A Survey (2020, background): “To enhance the session representation, attention mechanism is leveraged to integrate the whole sequence in addition to the most recent item [32, 35].”
User-oriented Fairness in Recommendation (2021, methodology): “• STAMP [28]: A session-based recommendation model based on attention mechanism, which can capture user’s long-term and short-term preferences.”
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (2019, methodology): “Recently, some works try to employ the attention mechanism to improve recommendation performances and interpretability [28, 33].”
Session-based Recommendation with Graph Neural Networks (2018, background): “…network (RNN) has been the most successful one in modeling sentences (Mikolov et al. 2010) and has been flourishingly applied in various natural language processing tasks, such as machine translation (Cho et al. 2014), conversation machine (Serban et al. 2016), and image caption (Mao et al. 2015).”
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System (2020, methodology): “For the sequential baselines, we introduce POP, BPR [57], GRU4Rec+ [26], STAMP [45], NARM [36], Caser [67], NextItNet [80], SRGNN [74] and BERT4Rec [63] for comparison.”
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (2019, methodology): “To alleviate the inﬂuence oftime order, STAMP [19] only uti-lizesthe self-attention mechanism withoutRNN.”