zero-one.geni.ml.recommendation
alsclj
(als params)Alternating Least Squares (ALS) matrix factorization.
ALS attempts to estimate the ratings matrix R as the product of two lower-rank matrices, X and Y, i.e. X * Yt = R. Typically these approximations are called 'factor' matrices. The general approach is iterative. During each iteration, one of the factor matrices is held constant, while the other is solved for using least squares. The newly-solved factor matrix is then held constant while solving for the other factor matrix.
This is a blocked implementation of the ALS factorization algorithm that groups the two sets of factors (referred to as "users" and "products") into blocks and reduces communication by only sending one copy of each user vector to each product block on each iteration, and only for the product blocks that need that user's feature vector. This is achieved by pre-computing some information about the ratings matrix to determine the "out-links" of each user (which blocks of products it will contribute to) and "in-link" information for each product (which of the feature vectors it receives from each user block it will depend on). This allows us to send only an array of feature vectors between each user block and product block, and have the product block find the users' ratings and update the products based on these messages.
For implicit preference data, the algorithm used is based on "Collaborative Filtering for Implicit Feedback Datasets", available at https://doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here.
Essentially instead of finding the low-rank approximations to the rating matrix R, this finds the approximations for a preference matrix P where the elements of P are 1 if r is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence' values related to strength of indicated user preferences rather than explicit ratings given to items.
Note: the input rating dataset to the ALS implementation should be deterministic. Nondeterministic data can cause failure during fitting ALS model. For example, an order-sensitive operation like sampling after a repartition makes dataset output nondeterministic, like dataset.repartition(2).sample(false, 0.5, 1618). Checkpointing sampled dataset or adding a sort before sampling can help make the dataset deterministic.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALS.html
Timestamp: 2020-10-19T01:56:00.419Z
Alternating Least Squares (ALS) matrix factorization. ALS attempts to estimate the ratings matrix R as the product of two lower-rank matrices, X and Y, i.e. X * Yt = R. Typically these approximations are called 'factor' matrices. The general approach is iterative. During each iteration, one of the factor matrices is held constant, while the other is solved for using least squares. The newly-solved factor matrix is then held constant while solving for the other factor matrix. This is a blocked implementation of the ALS factorization algorithm that groups the two sets of factors (referred to as "users" and "products") into blocks and reduces communication by only sending one copy of each user vector to each product block on each iteration, and only for the product blocks that need that user's feature vector. This is achieved by pre-computing some information about the ratings matrix to determine the "out-links" of each user (which blocks of products it will contribute to) and "in-link" information for each product (which of the feature vectors it receives from each user block it will depend on). This allows us to send only an array of feature vectors between each user block and product block, and have the product block find the users' ratings and update the products based on these messages. For implicit preference data, the algorithm used is based on "Collaborative Filtering for Implicit Feedback Datasets", available at https://doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here. Essentially instead of finding the low-rank approximations to the rating matrix R, this finds the approximations for a preference matrix P where the elements of P are 1 if r is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence' values related to strength of indicated user preferences rather than explicit ratings given to items. Note: the input rating dataset to the ALS implementation should be deterministic. Nondeterministic data can cause failure during fitting ALS model. For example, an order-sensitive operation like sampling after a repartition makes dataset output nondeterministic, like dataset.repartition(2).sample(false, 0.5, 1618). Checkpointing sampled dataset or adding a sort before sampling can help make the dataset deterministic. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALS.html Timestamp: 2020-10-19T01:56:00.419Z
alternating-least-squaresclj
(alternating-least-squares params)Alternating Least Squares (ALS) matrix factorization.
ALS attempts to estimate the ratings matrix R as the product of two lower-rank matrices, X and Y, i.e. X * Yt = R. Typically these approximations are called 'factor' matrices. The general approach is iterative. During each iteration, one of the factor matrices is held constant, while the other is solved for using least squares. The newly-solved factor matrix is then held constant while solving for the other factor matrix.
This is a blocked implementation of the ALS factorization algorithm that groups the two sets of factors (referred to as "users" and "products") into blocks and reduces communication by only sending one copy of each user vector to each product block on each iteration, and only for the product blocks that need that user's feature vector. This is achieved by pre-computing some information about the ratings matrix to determine the "out-links" of each user (which blocks of products it will contribute to) and "in-link" information for each product (which of the feature vectors it receives from each user block it will depend on). This allows us to send only an array of feature vectors between each user block and product block, and have the product block find the users' ratings and update the products based on these messages.
For implicit preference data, the algorithm used is based on "Collaborative Filtering for Implicit Feedback Datasets", available at https://doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here.
Essentially instead of finding the low-rank approximations to the rating matrix R, this finds the approximations for a preference matrix P where the elements of P are 1 if r is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence' values related to strength of indicated user preferences rather than explicit ratings given to items.
Note: the input rating dataset to the ALS implementation should be deterministic. Nondeterministic data can cause failure during fitting ALS model. For example, an order-sensitive operation like sampling after a repartition makes dataset output nondeterministic, like dataset.repartition(2).sample(false, 0.5, 1618). Checkpointing sampled dataset or adding a sort before sampling can help make the dataset deterministic.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALS.html
Timestamp: 2020-10-19T01:56:00.419Z
Alternating Least Squares (ALS) matrix factorization. ALS attempts to estimate the ratings matrix R as the product of two lower-rank matrices, X and Y, i.e. X * Yt = R. Typically these approximations are called 'factor' matrices. The general approach is iterative. During each iteration, one of the factor matrices is held constant, while the other is solved for using least squares. The newly-solved factor matrix is then held constant while solving for the other factor matrix. This is a blocked implementation of the ALS factorization algorithm that groups the two sets of factors (referred to as "users" and "products") into blocks and reduces communication by only sending one copy of each user vector to each product block on each iteration, and only for the product blocks that need that user's feature vector. This is achieved by pre-computing some information about the ratings matrix to determine the "out-links" of each user (which blocks of products it will contribute to) and "in-link" information for each product (which of the feature vectors it receives from each user block it will depend on). This allows us to send only an array of feature vectors between each user block and product block, and have the product block find the users' ratings and update the products based on these messages. For implicit preference data, the algorithm used is based on "Collaborative Filtering for Implicit Feedback Datasets", available at https://doi.org/10.1109/ICDM.2008.22, adapted for the blocked approach used here. Essentially instead of finding the low-rank approximations to the rating matrix R, this finds the approximations for a preference matrix P where the elements of P are 1 if r is greater than 0 and 0 if r is less than or equal to 0. The ratings then act as 'confidence' values related to strength of indicated user preferences rather than explicit ratings given to items. Note: the input rating dataset to the ALS implementation should be deterministic. Nondeterministic data can cause failure during fitting ALS model. For example, an order-sensitive operation like sampling after a repartition makes dataset output nondeterministic, like dataset.repartition(2).sample(false, 0.5, 1618). Checkpointing sampled dataset or adding a sort before sampling can help make the dataset deterministic. Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALS.html Timestamp: 2020-10-19T01:56:00.419Z
item-factorsclj
(item-factors model)Params:
Result: DataFrame
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.288Z
Params: Result: DataFrame Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html Timestamp: 2020-10-19T01:56:42.288Z
recommend-for-all-itemsclj
(recommend-for-all-items model num-users)Params: (numUsers: Int)
Result: DataFrame
Returns top numUsers users recommended for each item, for all items.
max number of recommendations for each item
a DataFrame of (itemCol: Int, recommendations), where recommendations are stored as an array of (userCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.310Z
Params: (numUsers: Int)
Result: DataFrame
Returns top numUsers users recommended for each item, for all items.
max number of recommendations for each item
a DataFrame of (itemCol: Int, recommendations), where recommendations are
stored as an array of (userCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.310Zrecommend-for-all-usersclj
(recommend-for-all-users model num-items)Params: (numItems: Int)
Result: DataFrame
Returns top numItems items recommended for each user, for all users.
max number of recommendations for each user
a DataFrame of (userCol: Int, recommendations), where recommendations are stored as an array of (itemCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.315Z
Params: (numItems: Int)
Result: DataFrame
Returns top numItems items recommended for each user, for all users.
max number of recommendations for each user
a DataFrame of (userCol: Int, recommendations), where recommendations are
stored as an array of (itemCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.315Zrecommend-for-item-subsetclj
(recommend-for-item-subset model items-df num-users)Params: (dataset: Dataset[_], numUsers: Int)
Result: DataFrame
Returns top numUsers users recommended for each item id in the input data set. Note that if there are duplicate ids in the input dataset, only one set of recommendations per unique id will be returned.
a Dataset containing a column of item ids. The column name must match itemCol.
max number of recommendations for each item.
a DataFrame of (itemCol: Int, recommendations), where recommendations are stored as an array of (userCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.317Z
Params: (dataset: Dataset[_], numUsers: Int)
Result: DataFrame
Returns top numUsers users recommended for each item id in the input data set. Note that if
there are duplicate ids in the input dataset, only one set of recommendations per unique id
will be returned.
a Dataset containing a column of item ids. The column name must match itemCol.
max number of recommendations for each item.
a DataFrame of (itemCol: Int, recommendations), where recommendations are
stored as an array of (userCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.317Zrecommend-for-user-subsetclj
(recommend-for-user-subset model users-df num-items)Params: (dataset: Dataset[_], numItems: Int)
Result: DataFrame
Returns top numItems items recommended for each user id in the input data set. Note that if there are duplicate ids in the input dataset, only one set of recommendations per unique id will be returned.
a Dataset containing a column of user ids. The column name must match userCol.
max number of recommendations for each user.
a DataFrame of (userCol: Int, recommendations), where recommendations are stored as an array of (itemCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.319Z
Params: (dataset: Dataset[_], numItems: Int)
Result: DataFrame
Returns top numItems items recommended for each user id in the input data set. Note that if
there are duplicate ids in the input dataset, only one set of recommendations per unique id
will be returned.
a Dataset containing a column of user ids. The column name must match userCol.
max number of recommendations for each user.
a DataFrame of (userCol: Int, recommendations), where recommendations are
stored as an array of (itemCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.319Zrecommend-itemsclj
(recommend-items model num-items)(recommend-items model users-df num-items)Params: (numItems: Int)
Result: DataFrame
Returns top numItems items recommended for each user, for all users.
max number of recommendations for each user
a DataFrame of (userCol: Int, recommendations), where recommendations are stored as an array of (itemCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.315Z
Params: (numItems: Int)
Result: DataFrame
Returns top numItems items recommended for each user, for all users.
max number of recommendations for each user
a DataFrame of (userCol: Int, recommendations), where recommendations are
stored as an array of (itemCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.315Zrecommend-usersclj
(recommend-users model num-users)(recommend-users model items-df num-users)Params: (numUsers: Int)
Result: DataFrame
Returns top numUsers users recommended for each item, for all items.
max number of recommendations for each item
a DataFrame of (itemCol: Int, recommendations), where recommendations are stored as an array of (userCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.310Z
Params: (numUsers: Int)
Result: DataFrame
Returns top numUsers users recommended for each item, for all items.
max number of recommendations for each item
a DataFrame of (itemCol: Int, recommendations), where recommendations are
stored as an array of (userCol: Int, rating: Float) Rows.
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.310Zuser-factorsclj
(user-factors model)Params:
Result: DataFrame
Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html
Timestamp: 2020-10-19T01:56:42.347Z
Params: Result: DataFrame Source: https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/ml/recommendation/ALSModel.html Timestamp: 2020-10-19T01:56:42.347Z
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