Petpaws: A Comprehensive Dataset and Recommender System for Canine and Feline Breeds

This research proposes a dataset and a recommender system for recommending canine and feline breeds, comprising one of the largest collections of its kind with 369 canine breeds and 69 feline breeds. The dataset is distinguished by its meticulous selection of breed-specific attributes, such as adaptability, trainability, weather conditions, economy, location, grooming demands, exercise needs, friendliness towards strangers, and other relevant factors. The proposed recommender system for canine and feline breeds utilizes a rank-based, content-based, and collaborative filtering approach for recommendation that incorporates user preferences to recommend breeds that best match their requirements. The system is trained using cosine similarity to optimize recommendation accuracy and enhance user satisfaction. This research represents a significant contribution to the field of pet recommendation systems and offers valuable insights into the selection and matching of canine and feline breeds to specific user needs.