Search task extraction using k-contour based recurrent deep graph clustering

Search engines must accurately predict the implicit intent of users to effectively guide their online search experience and assist them in completing their tasks. Users create time-ordered query logs by performing various queries on search engines to access desired information. Search task extraction groups queries with the same intent into unique clusters, whether these queries come from different tasks within the same session or from the same task across different sessions. Accurate identification of user intent improves the performance of search-guiding processes, including query suggestion, personalized search, and advertisement retrieval. Many existing methods focus on creating graphs that show relationships between queries. However, these methods typically cluster the graph using simple threshold-based techniques rather than leveraging graph topological structure features. Recent studies have introduced deep clustering layers to prevent the model size from growing as the number of queries increases. However, these models rely on labeled data and overlook modern embeddings from language models. We propose a novel k-contour-based graph convolutional network connective proximity clustering layer (CoGCN-C-CL) architecture that clusters graphs without requiring labeled data by leveraging graph topological properties. CoGCN-C-CL simultaneously learns query representations and search tasks. The k-contours identify distinct regions of the graph, while the graph convolutional network (GCN) exploits interactions between nodes within these regions. Experimental results demonstrate that CoGCN-C-CL outperforms existing state-of-the-art search task clustering methods on frequently used search task datasets.