Items-for-item recommendations 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.
If you have enabled signals and recommendations for a collection, then the default
COLLECTION_NAME_bpr_item_recs, a BPR Recommender job is already created and configured to produce items-for-item recommendations (as well as items-for-user recommendations).
This is the recommended job type, and produces better results with a short runtime. Download the
APPName_item_item_rec_pipelines_bpr.json file and import it to create the query pipeline that consumes this job’s output. See Fetch Items-for-Item Recommendations (Collaborative/BPR Method) for details.
|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.|
The Content-Based Recommender job computes item similarities based on their content, such as product descriptions. This is a useful option when you do not have enough signals for accurate results using the collaborative method described above. Download the
APPName_item_item_rec_pipelines_content.json file and import it to create the query pipeline that consumes this job’s output.
Once you have run the recommender job, see Fetch Content-Based-Items-for-Item Recommendations.