Analysing the possibilities of a needs-based house configurator

Sammanfattning: A needs-based configurator is a system or tool that assists users in customizing products based on their specific needs. This thesis investigates the challenges of obtaining data for a needs-based machine learning house configurator and identifies suitable models for its implementation. The study consists of two parts: first, an analysis of how to obtain data, and second, an evaluation of three models for implementing the needs-based solution. The analysis shows that collecting house review data for a needs-based configurator is challenging due to several factors, including how the housing market operates compared to other markets, privacy concerns, and the complexity of the buying process. To address this, future studies could consider alternative data sources, adding contextual data, and creating surveys or questionnaires. The evaluation of three models: DistilBERT, BERT fine-tuned for Swedish, and a CNN with a Swedish word embedding layer, shows that both the BERT models perform well on the generated dataset, while the CNN model underperformed. The Swedish BERT model performed the best, achieving high recall and precision metrics for k between 2 and 5. This thesis suggests that further research on needs-based configurators should focus on alternative data sources and more extensive datasets to improve performance.