embed_sessions
Trains an item2vec model on provided lists of items.
Lists of items may represent pages visited in a browsing session, shopping baskets and the products they contain, sentences of words, etc. The step calculates embeddings vectors for all item lists, such that two vectors are similar if their corresponding lists of items are similar. Similarity here is measured as an average over the individual items. Essentially, we first calculate embeddings vectors representing individual items (using word2vec), and then average over all items belonging to the same list/session.
As an example, consider a dataset containing shopping baskets. In this case the step will first calculate embeddings for individual products. The resulting vectors will be similar if they represent objects that are often bought together. E.g. the vectors for sausages and hot dog bread may be more similar to each other than those representing shampoo and toys. Then, to arrive at an embedding vector for each basket, we simply average over all its individual products. The result will capture the similarity between baskets in terms of the mix of products they contain. And so the vectors representing baskets of people buying a significant amount of baby products will be more similar to each other than to vectors representing baskets of people buying products for a BBQ party.
To only calculate individual item embeddings see the complementary embed_items step.
Also, we use gensim to train the item2vec model, so for further details also see it’s word2vec page.
Usage
The following example shows how the step can be used in a recipe.
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally
columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Configuration
The following parameters can be used to configure the behaviour of the step by including them in
a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).
Length of embedding vectors.
Values must be in the following range:
Use Skip-Gram or CBOW. Set this to 1 to use Skip-Gram, 0 for CBOW.
Values must be in the following range:
Update maximum for negative-sampling. Only update these many word vectors.
Initial Learning Rate.
Values must be in the following range:
Word context window. Must be either an integer or “auto”, “max” or “all”.
Minimum count of item in dataset. Otherwise filtered out.
Values must be in the following range:
Iterations. How many epochs to run the algorithm for.
Values must be in the following range:
Sample. Percentage of most-common items to filter out.
Values must be in the following range:
Whether to return normalized item vectors.
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