Related papers: Modeling Information Need of Users in Search Sessions

Modeling Information Need of Users in Search Sessions

URL: http://arxiv.org/abs/2001.00861v1
Date: Fri, 3 Jan 2020 15:25:45 GMT
Title: Modeling Information Need of Users in Search Sessions
Authors: Kishaloy Halder, Heng-Tze Cheng, Ellie Ka In Chio, Georgios Roumpos, Tao Wu, Ritesh Agarwal
Abstract summary: We propose a sequence-to-sequence based neural architecture that leverages the set of past queries issued by users. Firstly, we employ our model for predicting the words in the current query that are important and would be retained in the next query. We show that our intuitive strategy of capturing information need can yield superior performance at these tasks on two large real-world search log datasets.
Score: 5.172625611483604
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Users issue queries to Search Engines, and try to find the desired information in the results produced. They repeat this process if their information need is not met at the first place. It is crucial to identify the important words in a query that depict the actual information need of the user and will determine the course of a search session. To this end, we propose a sequence-to-sequence based neural architecture that leverages the set of past queries issued by users, and results that were explored by them. Firstly, we employ our model for predicting the words in the current query that are important and would be retained in the next query. Additionally, as a downstream application of our model, we evaluate it on the widely popular task of next query suggestion. We show that our intuitive strategy of capturing information need can yield superior performance at these tasks on two large real-world search log datasets.

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