Items-for-item recommendations use the Recommend Items for Item query stage to present items that are similar to a specified item. For example, when the user is viewing a BMX bicycle, Fusion can recommend other BMX bicycles. Similarity can be based on different criteria, such as click patterns, people who bought this also bought that, percentage match of document tags, and so on.
This is one type of content-based recommendation, which can also be used as input for producing collaborative recommendations.
If you have enabled signals and recommendations for a collection, then the default
<collection>_item_recommendations job is already created and configured to produce items-for-item recommendations (as well as items-for-user recommendations):
This is an ALS Recommender job.
|If you want to use different parameters for items-for-item recommendations and items-for-user recommendations, simply create separate jobs for each, where one job configuration includes an output collection for items-for-item recommendations only and the other includes an output collection for items-for-user recommendations only.|