Graph-Setes: A Graph Based Search Task Extraction Using Siamese Network

Abstract

Search task extraction is a sub-process for query suggestion/reformulation, personalized recommendation, and advertisement in search engines and e -commerce platforms. However, they face both internal and external challenges. Internal challenges include short and misspelled queries and incomplete keywords. External challenges include an unknown number of clusters and a limited number of labeled datasets. Deep-learning models require a large amount of data for training; however, search task datasets are rare and small. To overcome these limitations, we proposed Graph-SeTES, which integrates feature extraction with a decision network that utilizes both distance metrics and decision networks. The existing graph-clustering algorithms use the similarities between search query pairs. Because the Siamese network (SN) finds similarities between two objects, it fits search task extraction. Compared with existing models, SN requires fewer parameters for training due to the shared weights. However, it yields good results even with less labeled data, overcoming the external challenges. We benefit from both distance metrics and narrowing of the linear layers for decision networks. Graph-SeTES was compared with stateof -the -art models, and it outperformed its counterparts. The results were 6% better than those of the best baseline on the CSTE dataset, which maintained this performance difference on the WSMC12 dataset.