Query Intent Detection from the SEO Perspective

TL;DR

This paper presents a machine learning-based clustering approach that leverages Google search results and keyword extraction to accurately identify user query intents, enhancing SEO strategies and search satisfaction.

Contribution

It introduces a novel clustering model that uses features from search results and keyword extraction to determine query intent, improving upon previous methods.

Findings

High accuracy in intent detection using keyword-based clustering.

Effective use of Google search results to inform clustering.

Keywords significantly improve intent classification performance.

Abstract

Google users have different intents from their queries such as acquiring information, buying products, comparing or simulating services, looking for products, and so on. Understanding the right intention of users helps to provide i) better content on web pages from the Search Engine Optimization (SEO) perspective and ii) more user-satisfying results from the search engine perspective. In this study, we aim to identify the user query's intent by taking advantage of Google results and machine learning methods. Our proposed approach is a clustering model that exploits some features to detect query's intent. A list of keywords extracted from the clustered queries is used to identify the intent of a new given query. Comparing the clustering results with the intents predicted by filtered keywords show the efficiency of the extracted keywords for detecting intents.