NEBULA Framework: An Adaptive Framework for Unstructured Description to Solve Cold Start Problem

The cold start problem is one of the main challenges in recommendation systems, especially when the system has to provide recommendations for new items that do not yet have a history of interaction. Although various approaches have been developed, most still use conventional interaction-based methods, which are not optimal in providing accurate recommendations for new items that only have minimal and unstructured descriptive information. This research aims to provide recommendations for new items that lack interaction history and have unstructured descriptive information by addressing the cold start problem more adaptively. The proposed model is based on Named Entity Recognition (NER) and metadata representation as an adaptive framework capable of adjusting recommendation methods based on the availability of initial information. For new items, the system utilizes basic attributes such as product type, materials, and origin, and employs an adaptive approach for rating prediction. Testing results demonstrate system performance with an Accuracy of 0.967, Precision of 0.838, Recall of 0.846, F1-score of 0.842, and an average Mean Absolute Error (MAE) of 0.159. This adaptive framework proved to be superior to conventional approaches, with improvements in Precision of 15.59%, Recall of 17.50%, F1-score of 16.54%, and a significant reduction in MAE. Additionally, the Kappa value of 0.69 indicates a high level of agreement (substantial agreement) among validators. These findings demonstrate that the system is not only more accurate in recommending new items but also more reliable under minimal data conditions, thereby enhancing user confidence. Overall, this NER and metadata-based framework can serve as an effective solution for addressing the cold start problem and improving recommendation quality during the initial stages.