Evaluating Cold-Start in Recommendation Systems Using a Hybrid Model Based on Factorization Machines and SBERT Embeddings

Abstract: The item cold-start problem, which describes the difficulty of recommendation systems in recommending new items to users, remains a great challenge for recommendation systems that rely on past user-item interaction data. A popular technique in the current research surrounding the cold-start problem is the use of hybrid models that combine two or more recommendation strategies that may contribute with their individual advantages. This thesis investigates the use of a hybrid model which combines Sentence BERT embeddings with a recommendation model based on Factorization Machines (FM). The research question is stated as: How does a hybrid recommendation system based on Factorization Machines with frozen Sentence BERT embeddings perform in terms of solving the cold-start problem?. Three experiments were conducted to answer the research question. These involved finding an optimal pre-trained Sentence BERT model, investigating the difference in performance between an FM-model and a hybrid FM-model, as well as the difference in ranking of an item depending on whether or not the hybrid FM-model has been trained on the item. The results show that the best pre-trained Sentence BERT model for producing meaningful embeddings is the paraphrase-MiniLM-L3-v2 model, that a hybrid FM-model and a standard FM-model perform almost equally in terms of precision and recall at 50, and that there is a weak correlation between the item-frequency and how the hybrid FM-model ranks an item when trained and not trained on the item. The answer to the research question is that a recommendation model based on Factorization Machines with frozen Sentence BERT embeddings displays low precision at 50 and recall at 50 values with the given parameters in comparison to the values given in an optimal recommendation scenario. The hybrid FM-model shows cold-start potential due to displaying similar results to the standard FM-model, but these values are so low that further investigation with other parameters is needed for a clearer conclusion.