关于“Movie Recommendations in BigQuery ML 2.5”的评价

GD S. · 评论over 3 years之前

Nicholas R. · 评论over 3 years之前

Asha Tejaswini G. · 评论over 3 years之前

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King E. · 评论over 3 years之前

John D. · 评论over 3 years之前

Charlene B. · 评论over 3 years之前

some simple big query can not be processed... a bit disappointing: SELECT AVG(predicted_rating) as average_rating, movieId FROM ML.RECOMMEND(MODEL `cloud-training-prod-bucket.movies.movie_recommender`) group by movieId order by movieId LIMIT 100000 Query exceeded resource limits. This query used 24249 CPU seconds but would charge only 43M Analysis bytes. This exceeds the ratio supported by the on-demand pricing model. Please consider moving this workload to the flat-rate reservation pricing model, which does not have this limit. 24249 CPU seconds were used, and this query must use less than 11000 CPU seconds.

Jianing M. · 评论over 3 years之前

Kamran S. · 评论over 3 years之前

Peter C. · 评论over 3 years之前

Daniel L. · 评论over 3 years之前

Aditya P. · 评论over 3 years之前

Francesc G. · 评论over 3 years之前

SELECT * FROM ML.PREDICT(MODEL `cloud-training-prod-bucket.movies.movie_recommender`, ( WITH allUsers AS ( SELECT DISTINCT userId FROM movies.movielens_ratings ) SELECT 96481 AS movieId, ( SELECT title FROM movies.movielens_movies WHERE movieId=96481) title, userId FROM allUsers )) ORDER BY predicted_rating DESC LIMIT 100 it's not working properly with the error Scalar subquery produced more than one element I had to change with SELECT * FROM ML.PREDICT(MODEL `cloud-training-prod-bucket.movies.movie_recommender`, ( WITH allUsers AS ( SELECT DISTINCT userId FROM movies.movielens_ratings ) SELECT 96481 AS movieId, ( SELECT DISTINCT title FROM movies.movielens_movies WHERE movieId=96481) title, userId FROM allUsers )) ORDER BY predicted_rating DESC LIMIT 100

Oussama B. · 评论over 3 years之前

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Siva C. · 评论over 3 years之前