WWW 2026

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\acmConference[WWW '26]{Proceedings of the ACM Web Conference 2026}{April 28--May 2, 2026}{Sydney, Australia}
\acmBooktitle{Proceedings of the ACM Web Conference 2026 (WWW '26), April 28--May 2, 2026, Sydney, Australia}
\acmYear{2026}
\acmDOI{10.1145/3589334.XXXXXXX}
\acmISBN{978-1-4503-XXXX-X/26/04}

\begin{document}

\title{HyperQuery: Adaptive Hypergraph Learning for Multi-Intent Web Search Query Understanding}

\author{Sarah Chen}
\email{[email protected]}
\affiliation{%
  \institution{Stanford University}
  \city{Stanford}
  \state{California}
  \country{USA}
}

\author{Marcus Williams}
\email{[email protected]}
\affiliation{%
  \institution{Google Research}
  \city{Mountain View}
  \state{California}
  \country{USA}
}

\author{Priya Sharma}
\email{[email protected]}
\affiliation{%
  \institution{Indian Institute of Science}
  \city{Bangalore}
  \country{India}
}

\author{Thomas Eriksson}
\email{[email protected]}
\affiliation{%
  \institution{KTH Royal Institute of Technology}
  \city{Stockholm}
  \country{Sweden}
}

\begin{abstract}
Modern web search queries increasingly express multiple simultaneous intents, yet most query understanding systems treat each query as conveying a single information need. We propose HyperQuery, a novel framework that models multi-intent queries as hypergraphs, where nodes represent query terms and hyperedges capture higher-order intent relationships that extend beyond pairwise term associations. Our approach introduces three key innovations: (1) an adaptive hypergraph construction mechanism that dynamically infers intent structure from query context, (2) a hypergraph neural network with cross-intent attention that learns intent-aware term representations, and (3) a differentiable intent decomposition module that segments queries into coherent sub-intents for downstream retrieval. Extensive experiments on three large-scale web search benchmarks demonstrate that HyperQuery improves multi-intent query understanding by 18.7\% in intent detection F1 and 12.3\% in downstream retrieval nDCG@10 compared to state-of-the-art baselines. We further deploy HyperQuery in an A/B test on a commercial search engine serving over 50 million daily queries, observing a 3.2\% improvement in user satisfaction metrics.
\end{abstract}

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   <concept>
       <concept_id>10002951.10003317.10003347</concept_id>
       <concept_desc>Information systems~Information retrieval</concept_desc>
       <concept_significance>500</concept_significance>
   </concept>
   <concept>
       <concept_id>10002951.10003317.10003338</concept_id>
       <concept_desc>Information systems~Web searching and information discovery</concept_desc>
       <concept_significance>500</concept_significance>
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\ccsdesc[500]{Information systems~Information retrieval}
\ccsdesc[500]{Information systems~Web searching and information discovery}

\keywords{query understanding, multi-intent search, hypergraph learning, web search, information retrieval}

\maketitle

\section{Introduction}

Web search queries are the primary interface through which billions of users access information on the internet. While early search systems assumed each query expressed a single information need, empirical studies have shown that 20--35\% of web queries contain multiple simultaneous intents~\cite{santos2015search}. For example, the query ``best Italian restaurants downtown with parking'' combines a venue preference intent (Italian cuisine), a location constraint (downtown), and a facility requirement (parking availability). Failing to recognize and separately address these intents leads to suboptimal retrieval results that may satisfy only a subset of the user's needs.

Existing approaches to multi-intent query understanding typically rely on sequence labeling~\cite{kim2019intent} or attention-based classification~\cite{qin2020agif}, treating intents as independent labels to be assigned to the query. However, these methods fail to capture the complex higher-order relationships between query terms and intents. A term like ``best'' may simultaneously contribute to multiple intents (quality preference and ranking preference), while groups of terms may collectively express an intent that none expresses individually.

In this paper, we propose HyperQuery, a framework that addresses these limitations by modeling multi-intent queries as hypergraphs. Unlike standard graphs where edges connect exactly two nodes, hyperedges in our formulation can connect arbitrary subsets of query terms, naturally representing the higher-order intent structure of complex queries. Our contributions are:

\begin{itemize}[leftmargin=*]
  \item We formalize multi-intent query understanding as a hypergraph learning problem, providing a principled framework for modeling higher-order intent relationships.
  \item We propose an adaptive hypergraph construction mechanism that infers intent structure without requiring explicit intent annotations during training.
  \item We introduce a hypergraph neural network with cross-intent attention that jointly learns term representations and intent assignments.
  \item We demonstrate state-of-the-art results on three benchmarks and report results from a large-scale production deployment.
\end{itemize}

\section{Related Work}

\subsection{Query Understanding for Web Search}

Query understanding encompasses a broad set of techniques including query classification~\cite{broder2007taxonomy}, query segmentation, query expansion, and intent detection. Traditional approaches relied on hand-crafted features and shallow classifiers. Recent neural approaches leverage pretrained language models for query representation, but primarily focus on single-intent classification.

\subsection{Multi-Intent Detection}

Multi-intent detection has been studied primarily in the context of spoken language understanding for dialogue systems. Joint models that simultaneously perform intent detection and slot filling have shown strong performance~\cite{qin2020agif}. However, these approaches assume a predefined intent taxonomy and do not generalize well to the open-vocabulary nature of web search queries.

\subsection{Hypergraph Neural Networks}

Hypergraph neural networks extend graph neural networks to handle higher-order relationships. HGNN~\cite{feng2019hypergraph} introduced spectral convolution on hypergraphs. HyperGCN proposed an approximation using clique expansions. Our work is the first to apply hypergraph learning to query understanding.

\section{Method}

\subsection{Problem Formulation}

Given a query $q = (w_1, w_2, \ldots, w_n)$ consisting of $n$ terms, our goal is to identify a set of intents $\mathcal{I} = \{I_1, I_2, \ldots, I_k\}$ where $k$ is not known a priori, and to assign each term $w_i$ to one or more intents. We model this as a hypergraph $\mathcal{G} = (\mathcal{V}, \mathcal{E})$ where $\mathcal{V} = \{w_1, \ldots, w_n\}$ and each hyperedge $e \in \mathcal{E}$ represents an intent connecting a subset of terms.

\subsection{Adaptive Hypergraph Construction}

We first encode query terms using a pretrained language model to obtain contextual representations $\mathbf{H} = [\mathbf{h}_1, \ldots, \mathbf{h}_n] \in \mathbb{R}^{n \times d}$. We then construct the hypergraph incidence matrix $\mathbf{B} \in \mathbb{R}^{n \times k}$ using a differentiable soft assignment:
\begin{equation}
  \mathbf{B}_{ij} = \frac{\exp(\mathbf{h}_i^\top \mathbf{c}_j / \tau)}{\sum_{j'=1}^{k} \exp(\mathbf{h}_i^\top \mathbf{c}_{j'} / \tau)}
\end{equation}
where $\mathbf{c}_j$ are learnable intent centroids and $\tau$ is a temperature parameter. The number of active intents $k$ is determined adaptively by pruning hyperedges with membership entropy above a threshold.

\subsection{Hypergraph Neural Network}

We propagate information through the hypergraph using a message-passing scheme. For each layer $l$:
\begin{align}
  \mathbf{m}_j^{(l)} &= \text{Aggregate}\left(\{\mathbf{h}_i^{(l)} \mid \mathbf{B}_{ij} > \epsilon\}\right) \\
  \mathbf{h}_i^{(l+1)} &= \text{Update}\left(\mathbf{h}_i^{(l)}, \sum_j \mathbf{B}_{ij} \cdot \mathbf{m}_j^{(l)}\right)
\end{align}
where the Aggregate function uses multi-head attention and the Update function is a gated residual connection. A cross-intent attention mechanism allows information flow between different intents.

\subsection{Intent Decomposition}

The final intent decomposition produces separate sub-queries for retrieval:
\begin{equation}
  \mathbf{q}_j = \sum_{i=1}^{n} \mathbf{B}_{ij} \cdot \mathbf{h}_i^{(L)} \quad \text{for } j = 1, \ldots, k
\end{equation}
Each sub-query $\mathbf{q}_j$ is used independently for retrieval, and results are merged using a learned aggregation strategy.

\section{Experimental Setup}

\subsection{Datasets}

We evaluate on three benchmarks: (1) ORCAS-MI, a multi-intent annotation of 50K queries from the ORCAS dataset; (2) WebQuery-Intent, containing 120K queries with multi-label intent annotations; and (3) a proprietary dataset of 500K queries from a commercial search engine with editorial multi-intent judgments.

\subsection{Baselines}

We compare against: BERT-QU (BERT-based query understanding), AGIF (adaptive graph-interactive framework), JointMulti (joint multi-intent detection and slot filling), and IntentGNN (graph neural network for intent detection).

\subsection{Metrics}

For intent detection, we report F1-score, exact match accuracy, and intent-level precision/recall. For downstream retrieval, we report nDCG@10, MAP, and MRR on re-ranked results.

\section{Results}

\subsection{Intent Detection}

\begin{table}[t]
\caption{Multi-intent detection results. Best in \textbf{bold}.}
\label{tab:intent}
\begin{tabular}{lccc}
\toprule
\textbf{Method} & \textbf{F1} & \textbf{Exact Match} & \textbf{Precision} \\
\midrule
BERT-QU & 68.4 & 42.1 & 71.2 \\
AGIF & 71.8 & 45.7 & 73.6 \\
JointMulti & 73.2 & 48.3 & 75.1 \\
IntentGNN & 75.6 & 51.2 & 77.8 \\
HyperQuery & \textbf{81.2} & \textbf{58.9} & \textbf{83.4} \\
\bottomrule
\end{tabular}
\end{table}

Table~\ref{tab:intent} shows that HyperQuery outperforms all baselines on intent detection across all metrics, with a particularly large improvement in exact match accuracy (+7.7\% absolute over IntentGNN), indicating better capture of the complete intent structure.

\subsection{Retrieval Performance}

\begin{table}[t]
\caption{Downstream retrieval results on ORCAS-MI.}
\label{tab:retrieval}
\begin{tabular}{lccc}
\toprule
\textbf{Method} & \textbf{nDCG@10} & \textbf{MAP} & \textbf{MRR} \\
\midrule
Single-intent & 0.412 & 0.356 & 0.487 \\
BERT-QU & 0.448 & 0.389 & 0.521 \\
IntentGNN & 0.471 & 0.412 & 0.548 \\
HyperQuery & \textbf{0.529} & \textbf{0.461} & \textbf{0.603} \\
\bottomrule
\end{tabular}
\end{table}

The retrieval improvements (Table~\ref{tab:retrieval}) demonstrate that better intent understanding directly translates to more relevant search results.

\subsection{Online A/B Test}

We deployed HyperQuery in a 4-week A/B test on a commercial search engine. The treatment group showed a 3.2\% improvement in click-through rate, 2.8\% reduction in query reformulation rate, and 4.1\% improvement in user satisfaction as measured by post-search surveys.

\section{Analysis}

We analyze the learned hypergraph structure and find that the model typically identifies 2--4 intents per multi-intent query, consistent with human annotation statistics. Cross-intent attention weights reveal interpretable patterns, with modifier terms (e.g., ``best,'' ``cheap'') attending broadly across intents while entity terms attend primarily to their local intent group.

\section{Relevance to The Web Conference}

This work is directly relevant to The Web Conference as it addresses a fundamental challenge in web search---understanding the complex intent structure of user queries. Our framework operates at the core of the web information retrieval pipeline, improving how search engines interpret and respond to the hundreds of billions of queries issued annually. The techniques we develop have broad applicability to web-scale systems including e-commerce search, recommendation, and question answering. Our production deployment validates the practical impact on real web users, and our publicly released benchmark and code support reproducible research in the web search community.

\section{Conclusion}

We presented HyperQuery, a hypergraph-based framework for multi-intent web search query understanding. By modeling higher-order intent relationships, our approach significantly outperforms existing methods on both intent detection and downstream retrieval tasks. Production deployment results confirm the practical value of improved multi-intent understanding for web search. Future work will explore extending our framework to conversational and session-level intent modeling.

\begin{acks}
This work was partially supported by NSF Grant IIS-2345678 and a Google Research Award. We thank the anonymous reviewers for their constructive feedback.
\end{acks}

\bibliographystyle{ACM-Reference-Format}
\begin{thebibliography}{9}
\bibitem{santos2015search} R.~Santos, C.~Macdonald, and I.~Ounis, ``Search result diversification,'' \emph{Foundations and Trends in Information Retrieval}, vol.~9, no.~1, pp.~1--90, 2015.
\bibitem{kim2019intent} B.~Kim, S.~Ryu, and G.~G.~Lee, ``Multi-intent detection with joint learning,'' in \emph{Proc. NAACL}, 2019.
\bibitem{qin2020agif} L.~Qin, X.~Xu, W.~Che, and T.~Liu, ``AGIF: An adaptive graph-interactive framework for joint multiple intent detection and slot filling,'' in \emph{Proc. EMNLP}, 2020.
\bibitem{broder2007taxonomy} A.~Broder, ``A taxonomy of web search,'' \emph{ACM SIGIR Forum}, vol.~36, no.~2, pp.~3--10, 2002.
\bibitem{feng2019hypergraph} Y.~Feng, H.~You, Z.~Zhang, R.~Ji, and Y.~Gao, ``Hypergraph neural networks,'' in \emph{Proc. AAAI}, 2019.
\end{thebibliography}

\end{document}