The increasing amount of user traffic on Internet discussion forums has led to a huge amount of unstructured natural language data in the form of user comments. Most modern recommendation systems rely on manual tagging, relying on administrators to label the features of a class, or story, which a user comment corresponds to. Another common approach is to use pre-trained word embeddings to compare class descriptions for textual similarity, then use a distance metric such as cosine similarity or Euclidean distance to find top k neighbors. However, neither approach is able to fully utilize this user-generated unstructured natural language data, reducing the scope of these recommendation systems. This paper studies the application of domain adaptation on a transformer for the set of user comments to be indexed, and the use of simple contrastive learning for the sentence transformer fine-tuning process to generate meaningful semantic embeddings for the various user comments that apply to each class. In order to match a query containing content from multiple user comments belonging to the same class, the construction of a subquery channel for computing class-level similarity is proposed. This channel uses query segmentation of the aggregate query into subqueries, performing k-nearest neighbors (KNN) search on each individual subquery. RecBERT achieves state-of-the-art performance, outperforming other state-of-the-art models in accuracy, precision, recall, and F1 score for classifying comments between four and eight classes, respectively. RecBERT outperforms the most precise state-of-the-art model (distilRoBERTa) in precision by 6.97% for matching comments between eight classes.