How cold-start in Spark ALS is handled in production?
Extracted from the documentation on Collaborative Filtering in Spark using ALS:
By default, Spark assigns
NaNpredictions during ALSModel.transform when a user and/or item factor is not present in the model. This can be useful in a production system, since it indicates a new user or item, and so the system can make a decision on some fallback to use as the prediction.
While I am playing with this coldStartStrategy parameter on my sandbox, withals = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="drop") set during the training and with a user not present in the training data, I get nothing when I try to recommend products to this new user. But with als = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="nan") set during the training, the model provides some recommendations to him. My question is: how are the products recommended for the latter case?
apache-spark pyspark recommendation-engine cold-start
edited Nov 13 '18 at 17:32
Joel
1,5726719
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
add a comment |
Extracted from the documentation on Collaborative Filtering in Spark using ALS:
By default, Spark assigns
NaNpredictions during ALSModel.transform when a user and/or item factor is not present in the model. This can be useful in a production system, since it indicates a new user or item, and so the system can make a decision on some fallback to use as the prediction.
While I am playing with this coldStartStrategy parameter on my sandbox, withals = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="drop") set during the training and with a user not present in the training data, I get nothing when I try to recommend products to this new user. But with als = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="nan") set during the training, the model provides some recommendations to him. My question is: how are the products recommended for the latter case?
apache-spark pyspark recommendation-engine cold-start
edited Nov 13 '18 at 17:32
Joel
1,5726719
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
add a comment |
Extracted from the documentation on Collaborative Filtering in Spark using ALS:
By default, Spark assigns
NaNpredictions during ALSModel.transform when a user and/or item factor is not present in the model. This can be useful in a production system, since it indicates a new user or item, and so the system can make a decision on some fallback to use as the prediction.
While I am playing with this coldStartStrategy parameter on my sandbox, withals = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="drop") set during the training and with a user not present in the training data, I get nothing when I try to recommend products to this new user. But with als = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="nan") set during the training, the model provides some recommendations to him. My question is: how are the products recommended for the latter case?
apache-spark pyspark recommendation-engine cold-start
edited Nov 13 '18 at 17:32
Joel
1,5726719
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
Extracted from the documentation on Collaborative Filtering in Spark using ALS:
By default, Spark assigns
NaNpredictions during ALSModel.transform when a user and/or item factor is not present in the model. This can be useful in a production system, since it indicates a new user or item, and so the system can make a decision on some fallback to use as the prediction.
While I am playing with this coldStartStrategy parameter on my sandbox, withals = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="drop") set during the training and with a user not present in the training data, I get nothing when I try to recommend products to this new user. But with als = ALS(userCol="user_id", itemCol="doc_id", ratingCol="rating", coldStartStrategy="nan") set during the training, the model provides some recommendations to him. My question is: how are the products recommended for the latter case?
apache-spark pyspark recommendation-engine cold-start
apache-spark pyspark recommendation-engine cold-start
edited Nov 13 '18 at 17:32
Joel
1,5726719
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
edited Nov 13 '18 at 17:32
Joel
1,5726719
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
edited Nov 13 '18 at 17:32
Joel
1,5726719
edited Nov 13 '18 at 17:32
Joel
1,5726719
edited Nov 13 '18 at 17:32
Joel
1,5726719
1,5726719
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
asked Nov 13 '18 at 16:56
Rushdi ShamsRushdi Shams
2,0891526
2,0891526
add a comment |
add a comment |
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