	\begin{algorithmic}[1]
	\State Inputs: $M, K, \epsilon, \mathcal{P}$ (the page dataset)
	\State $H_P := \varnothing, \forall P\in \mathcal{P}$
	\State $\mathrm{nn\_model} := \mathrm{model\_init}()$
		\For {$epoch = 1,2,\ldots$}
		    \State $S_{train} := \varnothing$
			\For {\textbf{each} webpage $P \in \mathcal{P}$}
			    \State $P\triangleq (V, E)$, $V\triangleq V_{labelled}\cup V_{unlabelled}$
				\State $S_P := \mathrm{UniformRand}(V_{unlabelled}, \mathrm{n_{samples}}=M)$ 
				\If {$epoch = K$} %\Comment{At epoch $K$, add \emph{hard} elements to training} 
				
				    \For {$l:=1,\dots, L$}
				    \State $preds = \{\mathrm{nn\_model}(v)[l], \forall 
				    \mathrm{elements}\ v\in V\})$
				    \State $H_P := H_P\cup\mathrm{rank}(preds)[:K]$
				    \EndFor
				\EndIf
				\State $S_{train} := S_{train}\cup S_P \cup V_{labelled} \cup H_P$ 
				
			\EndFor
			\State Train nn\_model for $1$ epoch on dataset $S_{train}$.
		\EndFor
	\end{algorithmic} 

\begin{algorithmic}
[1]
\State Inputs: $M, K, \epsilon, \mathcal{P}$ (the page dataset)
\State $H_P := \varnothing, \forall P\in \mathcal{P}$
\State $\mathrm{nn\_model} := \mathrm{model\_init}()$
\For {$epoch = 1,2,\ldots$}
\State $S_{train} := \varnothing$
\For {\textbf{each} webpage $P \in \mathcal{P}$}
\State $P\triangleq (V, E)$, $V\triangleq V_{labelled}\cup V_{unlabelled}$
\State $S_P := \mathrm{UniformRand}(V_{unlabelled}, \mathrm{n_{samples}}=M)$
\If {$epoch = K$} %\Comment{At epoch $K$, add \emph{hard} elements to training}
\For {$l:=1,\dots, L$}
\State $preds = \{\mathrm{nn\_model}(v)[l], \forall
\mathrm{elements}\ v\in V\})$
\State $H_P := H_P\cup\mathrm{rank}(preds)[:K]$
\EndFor
\EndIf
\State $S_{train} := S_{train}\cup S_P \cup V_{labelled} \cup H_P$
\EndFor
\State Train nn\_model for $1$ epoch on dataset $S_{train}$.
\EndFor
\end{algorithmic}
{
    "title": "the klarna product page dataset: web element nomination with graph   neural networks and large language models",
    "id": "2111.02168",
    "abstract": "web automation holds the potential to revolutionize how users interact with the digital world, offering unparalleled assistance and simplifying tasks via sophisticated computational methods. central to this evolution is the web element nomination task, which entails identifying unique elements on webpages. unfortunately, the development of algorithmic designs for web automation is hampered by the scarcity of comprehensive and realistic datasets that reflect the complexity faced by real-world applications on the web. to address this, we introduce the klarna product page dataset, a comprehensive and diverse collection of webpages that surpasses existing datasets in richness and variety. the dataset features 51,701 manually labeled product pages from 8,175 e-commerce websites across eight geographic regions, accompanied by a dataset of rendered page screenshots. to initiate research on the klarna product page dataset, we empirically benchmark a range of graph neural networks (gnns) on the web element nomination task. we make three important contributions. first, we found that a simple convolutional gnn (gcn) outperforms complex state-of-the-art nomination methods. second, we introduce a training refinement procedure that involves identifying a small number of relevant elements from each page using the aforementioned gcn. these elements are then passed to a large language model for the final nomination. this procedure significantly improves the nomination accuracy by 16.8 percentage points on our challenging dataset, without any need for fine-tuning. finally, in response to another prevalent challenge in this field - the abundance of training methodologies suitable for element nomination - we introduce the challenge nomination training procedure, a novel training approach that further boosts nomination accuracy.",
    "categories": "cs.lg cs.cl cs.cv cs.hc cs.ir",
    "doi": "",
    "created": "2021-11-03",
    "updated": "2024-02-23",
    "authors": [
        "alexandra hotti",
        "riccardo sven risuleo",
        "stefan magureanu",
        "aref moradi",
        "jens lagergren"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2111.02168"
}
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