Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.

Automated summary

Recommender systems help users choose relevant items from a vast selection. The cold start problem, when new items or users are added without previous information, is a major challenge. This article introduces a multi-source dataset optimized for studying and addressing the cold start problem. It also presents a user behavior-driven algorithm using this dataset, which combines collaborative filtering and user-item classification. The results show accurate recommendations and establish the dataset as valuable for future research in recommender systems, particularly regarding the cold start problem.