Improving search results with machine learning : Classifying multi-source data with supervised machine learning to improve search results

• Main Author: Stakovska, Meri
• Format: Others
• Language: English
• Published: Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) 2018
• Subjects: Searcher Frustration; Information Retrieval; Search Results; Topic Classification; Machine Learning; Supervised Classification; Naive Bayes; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75598

Sony’s Support Application team wanted an experiment to be conducted by which they could determine if it was suitable to use Machine Learning to improve the quantity and quality of search results of the in-application search tool. By improving the quantity and quality of the results the team wanted to improve the customer’s journey. A supervised machine learning model was created to classify articles into four categories; Wi-Fi & Connectivity, Apps & Settings, System & Performance, andBattery Power & Charging. The same model was used to create a service that categorized the search terms into one of the four categories. The classified articles and the classified search terms were used to complement the existing search tool. The baseline for the experiment was the result of the search tool without classification. The results of the experiment show that the number of articles did indeed increase but due mainly to the broadness of the categories the search results held low quality.