Abstract: The disclosure herein describes a recommendation system utilizing a specialized domain-specific language model for generating cold-start recommendations in an absence of user-specific data based on a user-selection of a seed item. A generalized language model is trained using a domain-specific corpus of training data, including title and description pairs associated with candidate items in a domain-specific catalog. The language model is trained to distinguish between real title-description pairs and fake title-description pairs. The trained language model analyzes the title and description of the seed item with the title and description of each candidate item in the catalog to create a hybrid set of scores. The set of scores includes similarity scores and classification scores for the seed item title with each candidate item description and title. The scores are utilized by the model to identify candidate items maximizing similarity with the seed item for cold-start recommendation to a user.

Abstract: The disclosure herein describes a recommendation system utilizing a specialized domain-specific language model for generating cold-start recommendations in an absence of user-specific data based on a user-selection of a seed item. A generalized language model is trained using a domain-specific corpus of training data, including title and description pairs associated with candidate items in a domain-specific catalog. The language model is trained to distinguish between real title-description pairs and fake title-description pairs. The trained language model analyzes the title and description of the seed item with the title and description of each candidate item in the catalog to create a hybrid set of scores. The set of scores includes similarity scores and classification scores for the seed item title with each candidate item description and title. The scores are utilized by the model to identify candidate items maximizing similarity with the seed item for cold-start recommendation to a user.