\begin{algorithm}[htbp]  \label{ag:core}
\small
  \caption{Training Process of RESTC}
  \begin{algorithmic}[1]\label{ag:core}
    \Require
      Sessions $\mathbf{S}$, item embeddings $\mathbf{V_{s}}$ 
    \Ensure
      Top-k recommendation items
    \State  Transform session data into spatial and temporal view 
     \State Construct CFG overall sessions

    \For{epoch in range(Epoches)}
      \For{batch in DataLoader}
         \For{each session s in batch}
         \State \textbf{Spatio-Temporal Contrastive Learning task:}
        \State Spatial view embedding $\mathbf{G}(s) \gets$ Eq.(1) to (6)
        \State Temporal view embedding $\mathbf{T}(s) \gets$ Eq.(7) to (13)
        \State Contrastive loss $\mathcal{L}_{cont} \gets$ Eq.(14)
        \State \textbf{Prediction task:}
        \State CFG embedding $\mathbf{\tilde{Z}} \gets$ Eq.(15)
        \State Enhanced spatial embedding $\mathbf{H_{g}} \gets$ Eq.(16) 
        \State Embedding Fusion $\mathbf{S_{h}} \gets$ Eq.(17) to (19)
        \State Next-item Predition loss $\mathcal{L}_{main} \gets$ Eq.(20), (21)
        
          \EndFor
        \EndFor
        \State $\mathcal{L}=\mathcal{L}_{\text {main}}+\eta_{1} \mathcal{L}_{cont}+\eta_{2}\|\Theta\|_{2}^{2}$
        \State Using multi-task training to jointly optimize $\mathcal{L}$
    \EndFor
  \end{algorithmic}
\end{algorithm}

\begin{algorithm}[htbp]
\small
\caption{Training Process of RESTC}
\begin{algorithmic}[1]
\Require
Sessions $\mathbf{S}$, item embeddings $\mathbf{V_{s}}$
\Ensure
Top-k recommendation items
\State Transform session data into spatial and temporal view
\State Construct CFG overall sessions
\For{epoch in range(Epoches)}
\For{batch in DataLoader}
\For{each session s in batch}
\State \textbf{Spatio-Temporal Contrastive Learning task:}
\State Spatial view embedding $\mathbf{G}(s) \gets$ Eq.(1) to (6)
\State Temporal view embedding $\mathbf{T}(s) \gets$ Eq.(7) to (13)
\State Contrastive loss $\mathcal{L}_{cont} \gets$ Eq.(14)
\State \textbf{Prediction task:}
\State CFG embedding $\mathbf{\tilde{Z}} \gets$ Eq.(15)
\State Enhanced spatial embedding $\mathbf{H_{g}} \gets$ Eq.(16)
\State Embedding Fusion $\mathbf{S_{h}} \gets$ Eq.(17) to (19)
\State Next-item Predition loss $\mathcal{L}_{main} \gets$ Eq.(20), (21)
\EndFor
\EndFor
\State $\mathcal{L}=\mathcal{L}_{\text {main}}+\eta_{1} \mathcal{L}_{cont}+\eta_{2}\|\Theta\|_{2}^{2}$
\State Using multi-task training to jointly optimize $\mathcal{L}$
\EndFor
\end{algorithmic}
\end{algorithm}
{
    "title": "spatio-temporal contrastive learning enhanced gnns for session-based   recommendation",
    "id": "2209.11461",
    "abstract": "session-based recommendation (sbr) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (gnns) to encode the representations of pair-wise relations among items and their neighbors. some of the existing gnn-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. to address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called session-based recommendation with spatio-temporal contrastive learning enhanced gnns (restc). the idea is to supplement the gnn-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. furthermore, a novel global collaborative filtering graph (cfg) embedding is leveraged to enhance the spatial view in the main task. extensive experiments demonstrate the significant performance of restc compared with the state-of-the-art baselines e.g., with an improvement as much as 27.08% gain on hr@20 and 20.10% gain on mrr@20.",
    "categories": "cs.ir",
    "doi": "",
    "created": "2022-09-23",
    "updated": "2024-02-17",
    "authors": [
        "zhongwei wan",
        "xin liu",
        "benyou wang",
        "jiezhong qiu",
        "boyu li",
        "ting guo",
        "guangyong chen",
        "yang wang"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2209.11461"
}
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