BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data. BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to train a matrix factorization model, which is a common technique for recommender systems. Matrix factorization models learn the latent factors that represent the preferences of users and the characteristics of items, and use them to predict the ratings or interactions between users and items. You can use the CREATE MODEL statement to create a matrix factorization model in BigQuery ML, and specify the matrix_factorization option as the model type. You can also use the ML.RECOMMEND function to generate recommendations for new games based on the trained model. This solution requires the least amount of coding, as you only need to write SQL queries to train and use the model. References: The answer can be verified from official Google Cloud documentation and resources related to BigQuery and BigQuery ML.

BigQuery ML | Google Cloud

Using matrix factorization | BigQuery ML

ML.RECOMMEND function | BigQuery ML