RECENGINE Procedure

Recommender systems are information filters that predict and suggest items or content that users might like, based on their previous behaviors, preferences, and interactions, in order to provide tailored suggestions to individual users. By using data-driven algorithms, recommender systems help users discover relevant content in an environment that inundates them with information. Recommender systems play a crucial role in enhancing user experience, increasing content or product consumption, and driving sales by presenting users with items that they are more likely to purchase, watch, or engage with. By personalizing user experiences, they also help businesses optimize their marketing strategies and understand their customers better.

Applications of recommender systems are diverse and continually expanding, with new use cases being identified in the research literature every year. Business domains that use recommender systems include e-commerce, content delivery, retail, banking, health care, oil and gas, automotive, industrial automation, and many others. Recommender system applications have also been identified for other service use cases, including in the context of municipal government and electrical grid management.

The RECENGINE procedure implements several different recommender system models that perform the task of recommending items to users on the basis of historical iterations between users and items. For more information about these models, see the section Details: RECENGINE Procedure.