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      Content-Based Recommender Jobs (Experimental)

      Table of Contents

      Use this job when you want to compute item similarities based on their content, such as product descriptions.

      First, item content is vectorized; different vectorization methods are available. Then, similar items are selected based on cosine similarity ("nearest neighbor") between their vectors.

      At a minimum, you must specify these:

      • An ID for this job

      • The name of the training collection, that is, the collection with your content

      • An output collection; create a separate collection for this

      • The name of the ID field for documents in the training collection, such as item_id_s

      • The names of one or more content fields in the training collection

      Content-based recommendations dataflow

      Content-based recommendations dataflow

      Tuning tips

      • Configure Metadata fields for item-item evaluation to use those fields during evaluation to determine whether pairs belong to the same category.

      • Perform approximate nearest neighbor search is enabled by default to significantly reduce the job’s running time, with a small decrease in accuracy. If your training dataset is very small, then you can disable this option.

      • If your content contains a lot of domain-specific jargon, enable Use Word2Vec for vectorization.

      • If your documents are too short or too long, enable Use TF-IDF for vectorization.

      Query pipeline setup

      Download the APPName_item_item_rec_pipelines_content.json file and import it to create the query pipeline that consumes this job’s output. See Fetch Content-Based Items-for-Item Recommendations for details.

      Loading configuration schema...