Matching the approach to your data
Recommendation methods range from simple to elaborate. Popularity and rules-based lists are a legitimate baseline. Collaborative filtering uses patterns in who interacted with what, content-based methods use item attributes, and hybrids combine both. Two-tower neural retrieval followed by a ranking model is common at larger scale. Choosing carefully matters because a complex model that adds latency can reduce conversion. Each step is reviewed with your team.
The right choice depends on how much interaction history you have, how often the catalog changes, and what signals exist beyond clicks, such as purchases, watch time, saves or returns. We start with a baseline and add complexity only where an experiment shows it improves the outcome you care about. Item features such as text, images and categories are embedded so similar items can be found even without interaction data.
