Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques

Abstract

Studying searcher behavior has been a cornerstone of search engine research for decades, since it can lead to a better understanding of user needs and allow for an improved user experience. Going beyond descriptive data analysis and statistics, studies have been utilizing the capabilities of Machine Learning to further investigate how users behave during general purpose searching. But the thematic content of a search greatly affects many aspects of user behavior, which often deviates from general purpose search behavior. Thus, in this study, emphasis is placed specifically on the fields of Art and Cultural Heritage. Insights derived from behavioral data can help Culture and Art institutions streamline their online presence and allow them to better understand their user base. Existing research in this field often focuses on lab studies and explicit user feedback, but this study takes advantage of real usage quantitative data and its analysis through machine learning. Using data collected by real world usage of the Art Boulevard proprietary search engine for content related to Art and Culture and through the means of Machine Learning-powered tools and methodologies, this article investigates the peculiarities of Art-related online searches. Through clustering, various archetypes of Art search sessions were identified, thus providing insight on the variety of ways in which users interacted with the search engine. Additionally, using extreme Gradient boosting, the metrics that were more likely to predict the success of a search session were documented, underlining the importance of various aspects of user activity for search success. Finally, through applying topic modeling on the textual information of user-clicked results, the thematic elements that dominated user interest were investigated, providing an overview of prevalent themes in the fields of Art and Culture. It was established that preferred results revolved mostly around traditional visual Art themes, while academic and historical topics also had a strong presence.